CN110119462A - A kind of community search method of net with attributes - Google Patents
A kind of community search method of net with attributes Download PDFInfo
- Publication number
- CN110119462A CN110119462A CN201910266196.1A CN201910266196A CN110119462A CN 110119462 A CN110119462 A CN 110119462A CN 201910266196 A CN201910266196 A CN 201910266196A CN 110119462 A CN110119462 A CN 110119462A
- Authority
- CN
- China
- Prior art keywords
- vertex
- node
- core
- circle
- cur
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 48
- 238000013507 mapping Methods 0.000 claims description 20
- 238000000354 decomposition reaction Methods 0.000 claims description 13
- 238000004590 computer program Methods 0.000 claims description 4
- 230000001174 ascending effect Effects 0.000 claims description 3
- 238000012512 characterization method Methods 0.000 claims description 3
- 238000004458 analytical method Methods 0.000 abstract description 2
- 230000003542 behavioural effect Effects 0.000 abstract description 2
- 201000010099 disease Diseases 0.000 abstract description 2
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 11
- 238000009826 distribution Methods 0.000 description 8
- 238000002474 experimental method Methods 0.000 description 8
- 230000000694 effects Effects 0.000 description 6
- 238000009966 trimming Methods 0.000 description 6
- 230000008569 process Effects 0.000 description 5
- 238000011160 research Methods 0.000 description 4
- 238000012360 testing method Methods 0.000 description 4
- 238000005054 agglomeration Methods 0.000 description 3
- 230000002776 aggregation Effects 0.000 description 3
- 238000013480 data collection Methods 0.000 description 3
- 101100379079 Emericella variicolor andA gene Proteins 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 241000208340 Araliaceae Species 0.000 description 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 1
- 235000003140 Panax quinquefolius Nutrition 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 235000008434 ginseng Nutrition 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000004850 protein–protein interaction Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/901—Indexing; Data structures therefor; Storage structures
- G06F16/9024—Graphs; Linked lists
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/907—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/909—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using geographical or spatial information, e.g. location
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/951—Indexing; Web crawling techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/954—Navigation, e.g. using categorised browsing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/01—Social networking
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Databases & Information Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Computing Systems (AREA)
- Human Resources & Organizations (AREA)
- Remote Sensing (AREA)
- Radar, Positioning & Navigation (AREA)
- Health & Medical Sciences (AREA)
- Economics (AREA)
- General Health & Medical Sciences (AREA)
- Library & Information Science (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Software Systems (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The present invention provides a kind of community search method of net with attributes.This method comprises: delimiting region of search range according to the spatial position of the network user；The spatial position of user in target community and the target community is being searched within the scope of the region of search delimited according to the connection tightness between the network user in net with attributes.The target community for meeting structure cohesiveness and space cohesiveness can be effectively searched out according to the method for the present invention, for the behavioural analysis of social network user, recommended, disease forecasting etc..
Description
Technical field
The present invention relates to community search technical field more particularly to a kind of community search methods of net with attributes.
Background technique
Net with attributes is for simulating various networks, including social networks, knowledge graph and protein-protein interaction network etc..More
Huge challenge is proposed to community search come the rich properties of more data volume and these networks, is caused in recent years very much
Concern.Community's detection and community search can be divided into about the research for finding community.Community detection method is commonly used in based on pre-
Community in the implicit standard discovery social networks of definition, and community search is to find that meet given one group bright with online mode
The community for having cohesiveness of true standard, such as the community search based on k-core (k core) and k-truss.
Space attribute is one of most important and the most useful feature in net with attributes.In spatial perception network, Mei Gejie
Point all has spatial information, for example, the social networks such as Twitter and Foursquare can be by such network modelling, wherein
Each node (i.e. user) has one or more positions (for example, current location or history registration location).By considering user
Location information search for community, can will come true to the understanding of user behavior from virtual world.
However, in the prior art, usually only considering non-net with attributes, and have ignored the abundant letter on vertex in net with attributes
Breath.In addition, coming the community in the sensing network of search space, in existing research, knot using various structure cohesiveness measurement
Structure cohesiveness is a kind of inquiry constraint, for example, measuring for k-core or k-truss, user needs to specify k in community search
Value, but do not account for the space tightness between user.
Therefore, it is necessary to be improved to the prior art, to search out the network for taking into account structure cohesiveness and space tightness
Community, and further increase the efficiency of community search.
Summary of the invention
It is an object of the invention to overcome the defect of the above-mentioned prior art, a kind of community search side of net with attributes is provided
Method.
According to the first aspect of the invention, a kind of community search method of net with attributes is provided.Should, method includes:
Step S1: region of search range delimited according to the spatial position of the network user；
Step S2: target community is being searched for according to the connection tightness between the network user in net with attributes, wherein described
The spatial position of user is within the scope of the region of search delimited in target community.
In one embodiment, step S1 includes following sub-step:
Carry out characterization attributes network with Connected undigraph G=(V, E, S), wherein V indicates that vertex set, E indicate that side collection, S indicate
Set of spatial locations, the vertex representation network user；
In the Connected undigraph G, the target community indicated with connected subgraph is searched for, wherein the vertex position of the subgraph
The circle encirclement that can be D by diameter and other subgraphs relative to the Connected undigraph G are set, vertex is formed most in the subgraph
The k-core of high-order.
In one embodiment, in step s 2, the target community indicated with connected subgraph is searched for according to following steps:
Step S21: for the Connected undigraph G, quaternary tree index structure is constructed, wherein root node corresponds to the entire of G
Space；
Step S22: traversing the quaternary tree index structure, obtains side length less than the side length of D and its father node greater than D's
These nodes are stored in node listing nodeList by all nodes；
Step S23: for each node in node listing nodeList, maximum nucleus number k is obtainedcur；
Step S24: N.DistMap [k is trimmed from node listingcurThe node N of] > D, wherein N.DistMap [kcur]
Indicate node N apart from mapping table；
Step S25: for the remaining node in nodeList, carrying out ascending sort according to the nucleus number upper bound and successively verify,
To search out k-core of the satisfaction with most high-order and can be surrounded by diameter for the circle of D.
In one embodiment, in step s 25, for a node N in node listing nodeList, using following step
Suddenly it is verified:
N is extended with length D, carry out nuclear decomposition in the square area of extension and ignores nucleus number less than kcurVertex；
The remaining vertex verified in the square area of extension is higher than k with the presence or absence of ordercurK-core, if it does,
It then records the k-core and updates kcur。
In one embodiment, rank whether there is using the remaining vertex in the square area of following steps verifying extension
Number is higher than kcurK-core:
For a vertex in node N, place it on the boundary for the circle that diameter is D and rotational circle；
When there is new summit to enter bowlder, order is checked for higher than kcurK-core.
In one embodiment, rank whether there is using the remaining vertex in the square area of following steps verifying extension
Number is higher than kcurK-core:
The square area of extension is divided into m × m cell, using can surround the covering s of the circle that diameter is D ×
The square of s cell is come the k-core that searches in extended square area, wherein s, m are positive integer and s is less than
m。
In one embodiment, rank whether there is using the remaining vertex in the square area of following steps verifying extension
Number is higher than kcurK-core:
For a vertex in node N, place it on the boundary for the circle that diameter is D and rotational circle；
In rotational circle, when the new summit for entering circle meets kcIt when-core, stops rotating, wherein kcIndicate current authentication
Nucleus number.
In one embodiment, the target community indicated with connected subgraph is searched for according to following steps:
The circle that all diameters are D is searched in shown Connected undigraph G；
For searching all circles, the maximum kernel order on the vertex surrounded can be justified and will have maximum kernel order by checking
The vertex that is surrounded of circle as the target community.
Compared with the prior art, the advantages of the present invention are as follows: the present invention provides the co-located communities with structure cohesiveness
The solution of search；And during community search, by constructing index structure, by spatial information and local structural information
Integrate the efficiency and validity for improving community search.
Detailed description of the invention
The following drawings only makees schematical description and interpretation to the present invention, is not intended to limit the scope of the present invention, in which:
Fig. 1 is the flow chart of the community search method of net with attributes according to an embodiment of the invention；
Fig. 2 is the schematic diagram of net with attributes according to an embodiment of the invention and co-located community；
Fig. 3 is the schematic diagram of the k-core quaternary tree of perceived distance according to an embodiment of the invention；
Fig. 4 is schematic diagram of the building according to an embodiment of the invention apart from mapping table；
Fig. 5 is the schematic diagram of the co-located community search according to an embodiment of the invention based on quaternary tree；
Fig. 6 is the schematic diagram of the co-located community search according to another embodiment of the present invention based on quaternary tree；
Fig. 7 (a) to Fig. 7 (c) is the relevance signal of diameter and community search time according to an embodiment of the invention
Figure；
Fig. 8 (a) to Fig. 8 (b) is the association of user location number and community search time according to an embodiment of the invention
Property schematic diagram；
Fig. 9 (a) to Fig. 9 (b) is position distribution and the community search time of user according to an embodiment of the invention
Relevance schematic diagram；
Figure 10 (a) to Figure 10 (b) is the effect picture of scalability according to an embodiment of the invention.
Specific embodiment
It is logical below in conjunction with attached drawing in order to keep the purpose of the present invention, technical solution, design method and advantage more clear
Crossing specific embodiment, the present invention is described in more detail.It should be appreciated that specific embodiment described herein is only used for explaining
The present invention is not intended to limit the present invention.
It is as shown herein and discuss all examples in, any occurrence should be construed as merely illustratively, without
It is as limitation.Therefore, other examples of exemplary embodiment can have different values.
Technology, method and apparatus known to person of ordinary skill in the relevant may be not discussed in detail, but suitable
In the case of, the technology, method and apparatus should be considered as part of specification.
One of goal in research of the invention, which is to provide, most has the search problem of the co-located community of cohesiveness (referred to herein as
MC3, the most cohesive co-located community), wherein the community searched for meets following two category
Property: structure cohesiveness refers to that the member contact in community is most close；Space is co-located, refers to that member is closer to each other on geographical location, tool
There is space cohesiveness.
According to one embodiment of present invention, the community search method of home network is provided, in short, this method utilizes
Connected undigraph carrys out characterization attributes network, meets structure cohesiveness and space cohesiveness standard by searching in Connected undigraph
Connected subgraph determine searched for target community.Specifically, shown in Figure 1, method includes the following steps:
Step S110 indicates net with attributes using Connected undigraph.
In embodiments of the present invention, it is described for characterizing undirected net with attributes G=(V, E, S) with Connected undigraph,
Wherein, G has vertex set V, side collection E and set of spatial locations S.In G the degree of vertex v (for example, user in social networks) by
DegG (v) indicates that each vertex v has spatial position v.l=(x, y) ∈ S (for example, registration location of user), x and y difference
Indicate the coordinate in two-dimensional space along x-axis and y-axis.
For ease of description, symbol definition of the present invention is summed up as follows:
G (V, E, S): the geography society figure with vertex set V, side collection E and set of spatial locations S is indicated；
(v.x, v.y): indicate a vertex v in vertex set V along the position of x-axis and y-axis；
DegG (v): the degree of a vertex v in G is indicated.
γ (N): the side length of index structure interior joint N is indicated.
Goal in research of the invention is that the community indicated by connected subgraph, the community are found from Connected undigraph G
Meet the following conditions: structure cohesiveness, i.e., the vertex connection in connected subgraph are most intensive；Space cohesiveness, i.e., in connected subgraph
Vertex it is spatially very compact.
In embodiments of the present invention, the assessment of structure cohesiveness is illustrated by taking k-core as an example, it should be understood that
It is that method of the invention also can be generalized to the algorithm of other commensurate structure cohesiveness such as k-truss, clique.
For ease of description, following concept is introduced first:
1), the definition of k-kore
For k-core, nonnegative integer k is given, the k-core of G is the clique of G, wherein each vertex v in the subgraph
Degree be not less than k.
Specifically, in embodiments of the present invention, (G is expressed as using the connection k-core in Gk) indicate community, referred to as Gk
Rank be k.A given figure, k-core can be obtained by algorithm in the prior art, for example, linear kernel decomposition algorithm, line
The complexity of property nuclear decomposition algorithm is denoted herein as O (| E |) (for example, bibliography " An o (m) algorithm for
Cores decomposition of networks ", Batagelj, V., Zaversnik, M., arXiv preprint cs/
0310049(2003))。
2), the definition of nucleus number
For giving the vertex v in G, nucleus number is the most high-order of the k-core comprising v, is expressed as CG[v]。
3), the definition of co-located community
In embodiments of the present invention, co-located community refers to connected subgraph (k-core) Gk, wherein the vertex position in the subgraph
Setting can be surrounded by the circle of predetermined diameter D.Vertex position herein desirably in co-located community is closer, this is able to reflect this
" the co-located property " of a community.
In embodiments of the present invention, undirected attributed graph G and diameter D, co-located community search (MC are given3) return to any vertex
Group and its position, meet following constraint: the position on vertex can be that the circle of D is surrounded with diameter；The k- of vertex formation most high-order
core。
Fig. 2 is the schematic diagram of net with attributes and co-located community, wherein C1And C2It is two co-located communities in net with attributes,
The circle that its member can be D by diameter surrounds, C1Member include A, B, C, C2Member include D, G, H, F, E.C2It is 3-core,
It is the core (about diameter D) in two co-located communities with most high-order, therefore C2It is the MC of the net with attributes3, that is, to be searched for
Target community.
Step S120, searches for connected subgraph in Connected undigraph, it is made to meet structure cohesiveness and space cohesiveness mark
It is quasi-.
It, can the present invention is directed to find the circle that the most community of structure cohesiveness and the community can be D by diameter to surround
To meet the community of structure cohesiveness and space cohesiveness standard using search in various embodiments dependence network.
Embodiment 1: space mode of priority
In this embodiment, for net with attributes, firstly, all possible circle (diameter D) on search space；Then,
Check the maximum kernel order on the vertex that can be surrounded by circle；Finally, returning to the maximum kernel order in all circles.
Specifically, all possible circle is enumerated, for example, two positions are fixed in set of spatial locations S, the two positions
Distance is less than or equal to D, spatially obtains the circle that most two diameters are D by the two positions.Then, from these positions
Vertex is obtained in affiliated figure, maximum core order is calculated using known linear kernel decomposition algorithm.This mode needs to examine
The circle under worst case is looked into, expense is O (V2), thus it is very time-consuming.
Embodiment 2: structure mode of priority
In this embodiment, structure first search is carried out, thought is using network structure come acceleration search.
Specifically, firstly, carrying out nuclear decomposition to calculate the nucleus number on each vertex；Then, the maximum k value in core, table are searched for
It is shown as kmax, and in kmaxIn-core, position is obtained from vertex；Next, by enumerating all possible circle in these positions
It is upper to execute search (being similar to the space mode of priority), after this step, current optimal core order can be obtained and (be expressed as
kcur)；Next, to (kmax- 1)-core is further checked.It repeats the above process, until reaching kcur-core.Pass through this side
Formula facilitates the verifying for reducing round quantity.However, since the overall situation has the subgraph of cohesiveness that may not have localized agglomeration power, because
This, there are still limitations for the execution efficiency of this mode.
Embodiment 3: the k core quaternary tree mode based on perceived distance
Above-mentioned space mode of priority and structure mode of priority is all unable to reach good performance, this is because MC3Problem
It needs to consider simultaneously space cohesiveness and structure cohesiveness, but both modes or has ignored the space characteristics of data or neglect
Structure feature is omited.
In a preferred embodiment, target community is scanned for using the k core quaternary tree of perceived distance, herein
Referred to as DkQ-TREE (Distance-aware k-core Quadtree).It can be constructed using quad-tree structure and precalculate office
The index of portion's structure cohesiveness, to acceleration search and trim search space.
Hereafter by the specific tree index structure for introducing the quaternary tree based on spatial index, and based on index structure proposition
Community search method solve MC3Problem.
1), the index structure of quaternary tree
Known linear k nuclear decomposition algorithm can only calculate the global nucleus number on vertex, therefore during inquiry, local cohesion
Force information is unknown.In the index structure based on quaternary tree, structural information and spatial information are combined to calculate
The cohesiveness of part (about diameter D).
Quad-tree structure is shown in Figure 3, in short, the method for building DkQ-TREE is that root node corresponds to entire space,
Entire space is divided into four sub-spaces, every sub-spaces correspond to a child node of root node.Then, it repeats each section
Point is separated into four child nodes, for example, entire space is root node (root), four child nodes point of the root node for Fig. 3
Not Dui Ying { A, B, C }, { K, J }, { L } and { D, E, F, G, H, I } similarly can further segment four child nodes.
In this embodiment, using quaternary tree and the space monotonicity based on localized agglomeration power precalculates each tree node
Localized agglomeration power and other useful informations.Space monotonicity refers to, area of space R (for example, square) is given, if the area
Vertex in domain is capable of forming the k-core of most h ranks, then for any region R ', the k- formed by the vertex in R ' in R
The order of core is not more than h.Space monotonicity attribute has less vertex based on lesser region.
In each node N of DkQ-TREE, each vertex of the node from the subgraph of the extracted region is precalculated
Nucleus number, and record the maximum nucleus number on vertex in node, be labeled as LCN.Executing this calculating is since following principle is (herein
In be known as lemma 1): the tree node N, MC that given inquiry diameter D and the circle that can be D by diameter surround3Order be not less than LCN。
Since the N circle that can be D by diameter surrounds, above-mentioned principle can be proved according to space monotonicity attribute.Therefore, root
According to the information precalculated MC can be obtained from DkQ-TREE3Rank Lower Bound Estimation.
However, this is still not enough to obtain local cohesiveness, the maximum nucleus number in each node can only be obtained.It can by Fig. 3
To find out, when some vertex are not on certain node, the vertex of the node can form a k-core.Therefore, for given
Diameter D, the boundary of the nucleus number of these nodes cannot be obtained.Therefore, distance mapping of the vertex in each tree is further calculated
Table DistMap.Thought is node N to be given, for each value k > LCN, by point spread to the vertex with minimum range d, make
It obtains the vertex being related to during extension and is capable of forming k-core, distance d and corresponding k are being recorded in mapping table.
Facilitate to trim search space (referred to herein as lemma 2) according to following principle apart from mapping table: assuming that current
MC3Optimal factor be kcur, inquiry diameter D and node N is given, if N.DistMap [kcur] > D, then N cannot be to MC3Contribution
Any vertex, wherein N.DistMap [kcur] indicate node N apart from mapping table, the optimal factor of N is kcur。
Space monotonicity attribute also can be used to prove, i.e., if N.DistMap [k in above-mentioned principlecur] > D, then mean
When extend N boundary length be diameter D when, can not still find k in this regioncur- core, therefore, any section in the region
The nucleus number of point is less than kcur, can be trimmed to about.
In addition, in order to also vertex mapping table can be used when vertex has multiple positions from position quick obtaining vertex
Organising map information.
To sum up, in embodiments of the present invention, for each node of DkQ-TREE, the information of storage includes: in the node
Vertex；Maximum nucleus number in the node；Vertex mapping table；Apart from mapping table.
2), the index construct of quaternary tree
Referring also to quad-tree structure as indicated at 3, entire space is root node (root), four child nodes of the root node
It respectively corresponding { A, B, C }, { K, J }, { L } and { D, E, F, G, H, I }, node { A, B, C } is further subdivided into { A }, { B }, { C },
Node { D, E, F, G, H, I } is further subdivided into { D }, { E }, { F } and { G, H, I }.When obtaining a new node, use first
Vertex in the node carries out nuclear decomposition and stores maximum nucleus number.If maximum nucleus number is less than some value kε, then not further
Split the node.For example, vertex { A, B, C } forms 2 cores in Fig. 3, then this region is divided, is formed { A }, { B } and { C }.?
After division, any subregion cannot all form 2 cores, therefore, stop splitting the corresponding node of these subregions.
In addition, also constructing it apart from mapping table (Distance Map) and vertex mapping table when obtaining a new node
(Vertex Map).Building vertex mapping table is the position for marking each vertex, for example, in Fig. 3, the position seal of vertex A
Record vAPosition be A (vA' s locations:A), it is other similar.The thought constructed apart from mapping table is, for each value k, to hold
Row binary search with by point spread to minimum range vertex so that the vertex introduced during extension is capable of forming k
Core.For example, with reference to shown in Fig. 4, node only has a vertex C, when expanding to vertex B, formed 1-core, extended away from
From being d1；When expanding to vertex A, it is initially formed 2-core, the distance extended is d2.Distance d1 and d2 are stored to distance
Mapping table, such as storage format are 1-core:d1；2-core:d2.
3), the community search method MC based on quaternary tree3Alg
In embodiments of the present invention, propose that two kinds of algorithms are referred to as to distinguish based on quaternary tree index structure
MC3Alg algorithm and MC3Alg+ algorithm, MC3Alg+ is MC3The improvement of Alg algorithm.
In short, MC3Alg algorithm is related to two iterative steps: the node in trimming DkQ-TREE；From the section that can not be trimmed
MC is found in point3.Specifically, MC3Alg the following steps are included:
Step S211 trims the node in DkQ-TREE
In this step, MC is obtained according to above-mentioned lemma 13The lower bound of order.
Specifically, given diameter D traverses DkQ-TREE from top to bottom, obtains side length and is less than D and the side length of its father node
All nodes greater than D.These nodes are stored in node listing nodeList.Then, from these sections in node listing
Point obtains maximum nucleus number and uses k as lower boundcurIt indicates.Use the MC3The lower bound of order is (i.e. given to look into according to lemma 2
Diameter D and node N is ask, if N.DistMap [kcur] > D, then N cannot be to MC3Contribute any vertex) further trimming
Node in nodeList.
Step S212 searches for target community from node remaining after trimming.
After trimming, for the remaining node in nodeList, according to from the nucleus number upper bound obtained apart from mapping table into
Then row sequence starts to verify optimal node N.
Specifically, node N is given, if N.distMap [k1]≤D≤N.distMap[k2], then k1It is the core on vertex in N
The number upper bound.Firstly, extending N with length D and carrying out nuclear decomposition in the square area of extension.It is then possible to safely ignore core
Number is less than kcurVertex because these vertex cannot be included in MC3In.In order to verify whether remaining vertex in extended area deposits
In the k-core with higher order, rather than all possible circle is checked as in the mode of priority of space.Implement at one
In example, using rotational circle method, basic thought is, for each vertex in node N, places it in the circle that diameter is D
On boundary, then, circle is rotated clockwise.When vertex enters bowlder, order is checked for higher than kcurK-core.If
In the presence of record k-core simultaneously updates kcur.For example, with reference to shown in Fig. 5, make vertex G be located at circle boundary on and rotate clockwise
Circle can find the 2-core formed by { G, F, H, I } when F enters bowlder.
K can be updated after verifying NcurAnd further according to updated kcurTrim the more more piece in nodeList
Then point executes verifying from next optimal node.It repeats the above process, until having handled all nodes in nodeList.
Further clearly to illustrate, following example 1 describes MC in the form of pseudocode3The frame of Alg.Firstly, from DkQ-
TREE (the 1st row) obtains nodeList；Then, MC is obtained3The lower bound of order, and N is stored using φmaxIn best k-core
(2-4 row), for each node in nodeList, obtain it apart from mapping table DistMap and check need to be expanded
Open up the distance comprising k-core；Knot removal (5-8 row) is safely carried out by lemma 2；Obtain vertex in the node
The nucleus number upper bound (the 9th row)；Next, being ranked up by the ascending order (the 10th row) in the node upper bound to nodeList, for each section
Point is extended it using length D and trims vertex as described above；For each vertex unpruned in N, rotational circle is used
Method checks k-core and updates φ (11-15 row).K-core with most high-order is ultimately stored in φ (the 16th row).
It include G referring also to giving shown in Fig. 5, the both candidate nodes of H, I make G on round boundary, and I, H, F, E, D exist
In round rotary area, the sequence for entering circle according to them obtains ordered list { I, H, F, E, D }.Then, circle is rotated clockwise,
When the vertex in { I, H, F, E, D } enters circle (on its boundary), rotation stops and checks inside it whether there is k-core.
For example, a 2-core ({ G, I, H, F }) can be obtained in circle when F enters bowlder.When circle rotates to vertex D, can obtain
To a 3-core ({ G, H, F, E, D }).In an identical manner after processing H and I, it can be seen that { G, H, F, E, D } is the section
With the k-core of most high-order in point.
For MC3Alg algorithm, computation complexity are analyzed as follows:
Assuming that average each unit space region includes n vertex and m edge, and obtained from the DkQ-TREE of given D
Obtain X node.
Firstly, being ranked up according to the nucleus number upper limit to node, complexity is O (XlogX).Then, for γ (N)=
Each node N of l, by N extension length D, i.e. γ (Nex)=2D+l, and nuclear decomposition is carried out in the square area.It is extending
Square in, there is (2D+l)2M edge, therefore nuclear decomposition cost is O ((2D+l)2m).Next, each top in N
Rotational circle on point.In each circle, haveA vertex andSide.
It note that the k-core executed in circle verifying can be divided into three steps:
Spending check cost isNuclear decomposition cost isBFS (breadth-first search) is checked
Cost isTherefore, k-core verifying cost is up toIn the worst case, in N
Most π D are executed for each vertex2(number of vertex in N is l to n times2n).Therefore, MC3The total complexity of Alg algorithm is
4), the community search method MC based on quaternary tree3Alg+
MC3Alg is still not efficient enough in large attribute network, and has limitation.This is because, firstly, each
In the node to be checked, there are many vertex, and each vertex is required using rotational circle method；Secondly, the expansion area of node
There are many vertex in domain, therefore need to verify many times k-core in rotational circle.In order to overcome these problems, providing one kind more has
The algorithm of effect, referred to herein as MC3Alg+。MC3Alg+ and MC3The main distinction between Alg is, the verifying cost of node,
And node trimming and MC in DkQ-TREE3Alg is identical.
In MC3In Alg+, for each node N to be checked, binary search is executed to find the maximum kernel in the node
Number.The upper limit of nucleus number obtains in mapping table from N's, with MC3Alg is similar, and lower limit is current optimal factor.It is searched at two points
During rope, check in the extended area of N with the presence or absence of with current nucleus number kcK-core.This mode can be quickly obtained
Biggish kc, have the beneficial effect that, firstly, reducing the vertex in the N detected；Second, reduce and draws in circle rotation
Vertex quantity in the extended area entered.
Next, the square area of extension is divided into m × m in order to be further reduced the vertex in the N to be checked
Cell, and the vertex that can not form solution is filtered out using a small square.Basic principle be or not directly by
A inspection vertex, but the square of covering s × s unit is used, it can surround the circle that diameter is D and carry out search extension just
All k-core in square region.It is mobile from the upper left corner of extension square area (including m × m cell) to the lower right corner
(s × s) square checks each position of square with the presence or absence of kc-core.Record includes kcAll squares of-core,
Round rotation only is carried out to the vertex i.e. in N and such square, wherein m, s are positive integer, and s is less than m, is actually answering
In, m and s appropriate can be set according to diameter of a circle, requirement to search granularity etc..In this fashion, verifying granularity is
Unit rather than vertex, therefore verifying speed is faster.
Finally, proposing two points of rotational circle methods of one kind to check candidate vertices, to improve verifying cost.With MC3Alg's
The main distinction is, in rotational circle, when new summit enters bowlder, will not stop rotating, but, use binary search strategy
Handle this problem.Specifically, it stops rotating when reaching such a vertex, from vertex is initially entered to the vertex, first
Meet kc-core.Then, the circle on boundary with the vertex is checked, if there is kc- core then records it and stops rotating；
Otherwise, it since the circle checked, finds and can satisfy kcNext vertex of-core.Due to that can skip not comprising any
The big region of core, therefore which is highly effective.
The example of binary search process shown in Figure 6 gives both candidate nodes identical with Fig. 4, is executed based on nucleus number
Binary search.There is upper bound upper=3 (from apart from mapping table) and lower bound lower=2 (current optimum value) first, therefore, when
Preceding nucleus number kcIt isThen, border vertices are set by vertex G, H, I.In rotary course, two points of strategies are considered.
Firstly, obtaining an ordered list { I, H, F, E, D } according to the sequence for entering search circle, ordered list is labeled as
InAngleList.Next, binary search is executed on InAngleList to find the vertex for meeting 2-core first.Because
Rotary area { G, H, I } forms 2-core, so find vertex H first, i.e., will circle rotate to H and find 2-core (i.e. G, H,
I}).It records and updates lower=2+1=3.Now, kcIt is 3, setting vertex G as border vertices and is repeated the above process.When
For vertex D when on the boundary of search circle, ({ G, D, E, F, H } forms 3-core to rotary area.Circle is directly rotated into vertex D, energy
It is enough that 3-core is found in circle, in this manner, it when rotating to F, E, will not stop, but be directly rotated to vertex D.Most
Afterwards, discovery { G, D, E, F, H } is the best core in the node.
For MC3Alg+ algorithm, computation complexity are analyzed as follows:
Make and M C3A l g is identical it is assumed that each expanding node N checked in needsex(γ(Nex)=2D+l) on hold
Row binary search.Assuming that from the maximum nucleus number obtained apart from mapping table be kmax, and be preferably at most logk to the binary search of kmax
It is secondary.The square area of extension is divided into T × T unit lattice, and filters out some vertex using the small square of covering s × s.
Small square coveringA vertex andA side needs to move small square (T-s)2It is secondary.Cause
This, the expense of moving process is up toFor each vertex in N, in two cyclotomy
In rotary course, expense is up to(each circle coveringA vertex andA side.Therefore, in the worst case, MC3Total complexity of Alg+ is
In order to further verify effect of the invention, emulation experiment has been carried out to assess the technical effect of above-described embodiment,
Wherein have evaluated the MC based on quaternary tree3Alg and MC3Alg+ algorithm and structure mode of priority, space mode of priority.But
Since structure mode of priority and the space mode of priority speed of service are very slow, its performance only is reported in one group of experiment below.It is real
It is as follows to test condition setting:
1), about the setting of data set
Experiment is utilized four data sets, including three real data sets (Gowalla, FourSquare, Flickr) and
One generated data collection (YoutubeSyn).In Gowalla, each vertex is the user in Gowalla, and each side indicates
Friendship between two users.Each user has many registrations, selects a most frequently used register information as his position.And
And in this data set there is the case where multiple registrations to be tested also directed to user.In FourSquare, each vertex
It is all the user of the website Foursquare, each side represents the social networks between two users.For each user, it is selected
Position of the most common register information as him.In Flickr, vertex is user, and side indicates " following " between two users
Relationship.Mark user in the position for wherein possessing most photo tokens.In YoutubeSyn, each vertex represents Youtube
User, each side is " to follow " relationship between two users.But the not no location information of user, it is raw for each user
At a position.In addition, in an experiment, position, including random distribution and Gaussian Profile are also generated using two kinds of location modes.
The details ginseng of data set is shown in Table 1, whereinIt is average degree, maxkIt is the maximum position number on node.
Table 1: data set attribute
2), about the setting of parameter.
10 are set by the quantity (quantity of the grid cell in expanded search region) of m, tests prove that the parameter is not
It can have much impact to performance, as m=10, realize the optimum operation time, therefore make in all experiments using m=10
For default value.In the experiment of multiple positions of user, for Gowalla, the position of user is all registrations of the user
Information.For YoutubeSyn, the random position for generating user.In different distribution experiments, generates two distributions of satisfaction and want
The position asked, including random distribution and Gaussian Profile.For all data sets, by position be placed on size be [0,100] × [0,
100] in square.
3), about experimental facilities.
It tests, is mounted on the machine of configuration Intel i7-6700 3.40GHz processor and 16GB memory
Windows10, and all algorithms are realized with java.
The experimental results showed that changing diameter D, there are multiple registration locations on a vertex, changes the factors such as user location distribution
The technical effect of the embodiment of the present invention can be had an impact.
Fig. 7 (a) to Fig. 7 (c) is the relevance schematic diagram of diameter and runing time, and specifically, changing diameter D will affect knot
Structure precedence method, space precedence method, MC3Alg and MC3The region of search of Alg+ and efficiency.Referring to shown in Fig. 7 (a) to Fig. 7 (c),
In, abscissa indicates diameter D, changes to 12.5 from 2.5 and (refers to for actual coordinate being transformed into [0,100] x's [0,100]
Coordinate behind square aearch region), ordinate indicates that runing time, unit are second (sec).Fig. 7 (a) to 7 (c) shows four
The runing time of kind algorithm, i.e. space precedence method (spatial), structure precedence method (structure), MC3Alg and MC3Alg+,
Fig. 7 (a) is the experimental result in data set Flickr, and Fig. 7 (b) is the experimental result of data set FourSquare, and Fig. 7 (c) is
The experimental result of data set Gowalla.It is observed that MC3Alg+ is always better than other algorithms, because it has most cut
Branch strategy and optimisation strategy, and space precedence method and structure sequence rule are very time-consuming, therefore will ignore in subsequent experiment
Both algorithms.
Fig. 8 (a) to 8 (b) is the relevance schematic diagram of positional number and runing time, wherein abscissa is positional number, indulges and sits
Mark is runing time (sec), and Fig. 8 (a) is the experimental result of data set YoutubeSyn, and Fig. 8 (b) is data set Gowalla
Experimental result.When there are multiple registration locations on a vertex, more registration locations will lead to more k-core and check.Cause
This, log-on count will affect MC3Alg and MC3The performance of Alg+.It is observed that MC3Alg+ by repeatedly register influenced it is smaller,
This is because executing binary search can speed up MC3The rotary course of Alg+.In addition, MC3The runing time ratio MC of Alg+3Alg is fast
About 7 times.
Fig. 9 (a) to 9 (b) is the relevance schematic diagram of position distribution and runing time, wherein abscissa is diameter value, is indulged
Coordinate is runing time, the Gaussian Profile of Fig. 9 (a) corresponding data collection YoutubeSyn, Fig. 9 (b) corresponding data collection
The random distribution of YoutubeSyn.It is observed that MC3Alg+ is better than MC always3Alg.It should be noted that MC3Alg+'s is superior
Property become apparent in Gaussian Profile, this is because some nodes include larger numbers of vertex, this leads to MC3Alg is being searched for
There is higher complexity when these nodes.
Figure 10 (a) to Figure 10 (b) is the effect picture of scalability, wherein abscissa is the percentage on vertex, is referred to whole
A data set number of vertex percentage (such as 20% expression certain data set number of vertex 20% scale Sub Data Set carry out reality
Test), ordinate is runing time, and Figure 10 (a) corresponds to Flickr data set, and the corresponding FourSquare data set of Figure 10 (b) passes through
Change two datasets to demonstrate the scalability of the embodiment of the present invention.It is observed that two kinds of algorithms can fit well
Answer data set size and MC3Alg+ also runs most fast due to there is more trimming strategies.
In conclusion for the search problem of the most co-located community of cohesiveness, the present invention provides various embodiments,
It is preferably based in the index structure (i.e. DkQ-TREE) of quaternary tree, spatial information and local structural information is integrated,
Accelerate the speed of target community's search.Also, it is based on DkQ-TREE, proposes two kinds of efficient algorithms, by true and conjunction
At data set progress, experimental results demonstrate the efficiency of the algorithm proposed and validity.The community search side of the embodiment of the present invention
Method can be used for the behavioural analysis of social network user, recommend, disease forecasting etc..
It should be noted that, although each step is described according to particular order above, it is not intended that must press
Each step is executed according to above-mentioned particular order, in fact, some in these steps can concurrently execute, or even is changed suitable
Sequence, as long as can be realized required function.In addition, those skilled in the art is in the premise without prejudice to spirit of that invention
Under, some embodiments can suitably be deformed, for example, with rotational circle counterclockwise, scale, Yong Huxu based on net with attributes
It asks, inquiry velocity requires that diameter D appropriate etc. is arranged.
The present invention can be system, method and/or computer program product.Computer program product may include computer
Readable storage medium storing program for executing, containing for making processor realize the computer-readable program instructions of various aspects of the invention.
Computer readable storage medium can be to maintain and store the tangible device of the instruction used by instruction execution equipment.
Computer readable storage medium for example can include but is not limited to storage device electric, magnetic storage apparatus, light storage device, electromagnetism and deposit
Store up equipment, semiconductor memory apparatus or above-mentioned any appropriate combination.The more specific example of computer readable storage medium
Sub (non exhaustive list) include: portable computer diskette, hard disk, random access memory (RAM), read-only memory (ROM),
Erasable programmable read only memory (EPROM or flash memory), static random access memory (SRAM), Portable compressed disk are read-only
Memory (CD-ROM), memory stick, floppy disk, mechanical coding equipment, is for example stored thereon with instruction at digital versatile disc (DVD)
Punch card or groove internal projection structure and above-mentioned any appropriate combination.
Various embodiments of the present invention are described above, above description is exemplary, and non-exclusive, and
It is not limited to disclosed each embodiment.Without departing from the scope and spirit of illustrated each embodiment, for this skill
Many modifications and changes are obvious for the those of ordinary skill in art field.The selection of term used herein, purport
In principle, the practical application or to the technological improvement in market for best explaining each embodiment, or make the art its
Its those of ordinary skill can understand each embodiment disclosed herein.
Claims (10)
1. a kind of community search method of net with attributes, comprising:
Step S1: region of search range delimited according to the spatial position of the network user；
Step S2: target community is being searched for according to the connection tightness between the network user in net with attributes, wherein the target
The spatial position of user is within the scope of the region of search delimited in community.
2. according to the method described in claim 1, wherein, step S1 includes following sub-step:
Carry out characterization attributes network with Connected undigraph G=(V, E, S), wherein V indicates that vertex set, E indicate side collection, S representation space
Position collection, the vertex representation network user；
In the Connected undigraph G, the target community indicated with connected subgraph is searched for, wherein the vertex position energy of the subgraph
Enough circles for being D by diameter surround and other subgraphs relative to the Connected undigraph G, and vertex forms most high-order in the subgraph
K-core.
3. according to the method described in claim 2, wherein, in step s 2, being searched for according to following steps is indicated with connected subgraph
Target community:
Step S21: for the Connected undigraph G, quaternary tree index structure is constructed, wherein root node corresponds to the entire sky of G
Between；
Step S22: traversing the quaternary tree index structure, obtains side length and is less than D and side length the owning greater than D of its father node
These nodes are stored in node listing nodeList by node；
Step S23: for each node in node listing nodeList, maximum nucleus number k is obtainedcur；
Step S24: N.DistMap [k is trimmed from node listingcurThe node N of] > D, wherein N.DistMap [kcur] indicate
Node N apart from mapping table；
Step S25: for the remaining node in nodeList, ascending sort is carried out according to the nucleus number upper bound and is successively verified, to search
Rope goes out to meet the k-core with most high-order and can be that the round of D surrounds by diameter.
4. according to the method described in claim 3, in step s 25, for a node N in node listing nodeList, adopting
It is verified with following steps:
N is extended with length D, carry out nuclear decomposition in the square area of extension and ignores nucleus number less than kcurVertex；
The remaining vertex verified in the square area of extension is higher than k with the presence or absence of ordercurK-core, if it is present note
It records the k-core and updates kcur。
5. according to the method described in claim 4, wherein, being pushed up using the residue in the square area of following steps verifying extension
Point is higher than k with the presence or absence of ordercurK-core:
For a vertex in node N, place it on the boundary for the circle that diameter is D and rotational circle；
When there is new summit to enter bowlder, order is checked for higher than kcurK-core.
6. according to the method described in claim 4, wherein, being pushed up using the residue in the square area of following steps verifying extension
Point is higher than k with the presence or absence of ordercurK-core:
The square area of extension is divided into m × m cell, it is a using the covering s × s that can surround the circle that diameter is D
The square of cell is come the k-core that searches in extended square area, wherein s, m are positive integer and s is less than m.
7. according to the method described in claim 4, wherein, being pushed up using the residue in the square area of following steps verifying extension
Point is higher than k with the presence or absence of ordercurK-core:
For a vertex in node N, place it on the boundary for the circle that diameter is D and rotational circle；
In rotational circle, when the new summit for entering circle meets kcIt when-core, stops rotating, wherein kcIndicate the core of current authentication
Number.
8. according to the method described in claim 2, wherein, the target society indicated with connected subgraph is searched for according to following steps
Area:
The circle that all diameters are D is searched in shown Connected undigraph G；
For searching all circles, the maximum kernel order on the vertex that can be surrounded by circle is checked and by the circle with maximum kernel order
The vertex surrounded is as the target community.
9. a kind of computer readable storage medium, is stored thereon with computer program, wherein real when the program is executed by processor
Now according to claim 1 to any one of 8 the method the step of.
10. a kind of computer equipment, including memory and processor, be stored on the memory to transport on a processor
Capable computer program, which is characterized in that the processor realizes any one of claims 1 to 8 institute when executing described program
The step of method stated.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910266196.1A CN110119462B (en) | 2019-04-03 | 2019-04-03 | Community search method of attribute network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910266196.1A CN110119462B (en) | 2019-04-03 | 2019-04-03 | Community search method of attribute network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110119462A true CN110119462A (en) | 2019-08-13 |
| CN110119462B CN110119462B (en) | 2021-07-23 |
Family
ID=67520775
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910266196.1A Active CN110119462B (en) | 2019-04-03 | 2019-04-03 | Community search method of attribute network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110119462B (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111274498A (en) * | 2020-01-22 | 2020-06-12 | 哈尔滨工业大学 | Network characteristic community searching method |
| CN111401517A (en) * | 2020-02-21 | 2020-07-10 | 华为技术有限公司 | Method and device for searching perception network structure |
| CN112445838A (en) * | 2020-10-23 | 2021-03-05 | 浙江工商大学 | Efficient space k-kernel mining method for space data |
| CN112818178A (en) * | 2019-10-30 | 2021-05-18 | 华东师范大学 | Fast and efficient community discovery method and system based on (k, p) -core |
| CN113254797A (en) * | 2021-04-19 | 2021-08-13 | 江汉大学 | Searching method, device and processing equipment for social network community |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070112781A1 (en) * | 2005-11-17 | 2007-05-17 | Mcmullen Cindy | System and method for providing search controls in a communities framework |
| CN103425662A (en) * | 2012-05-16 | 2013-12-04 | 腾讯科技（深圳）有限公司 | Information search method and device in network community |
| CN104462260A (en) * | 2014-11-21 | 2015-03-25 | 深圳大学 | Community search algorithm based on k-kernel |
| CN105339935A (en) * | 2013-04-17 | 2016-02-17 | 通腾导航技术股份有限公司 | Methods, devices and computer software for facilitating searching and display of locations relevant to a digital map |
| CN107247779A (en) * | 2017-06-08 | 2017-10-13 | 天津神笔马良网络科技有限公司 | Searching method, device and the mobile terminal of interaction community |
| CN108319728A (en) * | 2018-03-15 | 2018-07-24 | 深圳大学 | A kind of frequent community search method and system based on k-star |
-
2019
- 2019-04-03 CN CN201910266196.1A patent/CN110119462B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070112781A1 (en) * | 2005-11-17 | 2007-05-17 | Mcmullen Cindy | System and method for providing search controls in a communities framework |
| CN103425662A (en) * | 2012-05-16 | 2013-12-04 | 腾讯科技（深圳）有限公司 | Information search method and device in network community |
| CN105339935A (en) * | 2013-04-17 | 2016-02-17 | 通腾导航技术股份有限公司 | Methods, devices and computer software for facilitating searching and display of locations relevant to a digital map |
| CN104462260A (en) * | 2014-11-21 | 2015-03-25 | 深圳大学 | Community search algorithm based on k-kernel |
| CN107247779A (en) * | 2017-06-08 | 2017-10-13 | 天津神笔马良网络科技有限公司 | Searching method, device and the mobile terminal of interaction community |
| CN108319728A (en) * | 2018-03-15 | 2018-07-24 | 深圳大学 | A kind of frequent community search method and system based on k-star |
Non-Patent Citations (3)
| Title |
|---|
| JIEHUAN LUO 等: "Efficient Search of the Most Cohesive Co-located Community in Attributed Networks", 《INTERNATIONAL CONFERENCE ON DATABASE SYSTEMS FOR ADVANCED APPLICATIONS》 * |
| YIXIANG FANG 等: "Effective Community Search over Large Spatial Graphs", 《PROCEEDINGS OF THE VLDB ENDOWMENT》 * |
| 竺俊超 等: "复杂条件下的社区搜索算法", 《软件学报》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112818178A (en) * | 2019-10-30 | 2021-05-18 | 华东师范大学 | Fast and efficient community discovery method and system based on (k, p) -core |
| CN111274498A (en) * | 2020-01-22 | 2020-06-12 | 哈尔滨工业大学 | Network characteristic community searching method |
| CN111401517A (en) * | 2020-02-21 | 2020-07-10 | 华为技术有限公司 | Method and device for searching perception network structure |
| CN111401517B (en) * | 2020-02-21 | 2023-11-03 | 华为技术有限公司 | Method and device for searching perceived network structure |
| CN112445838A (en) * | 2020-10-23 | 2021-03-05 | 浙江工商大学 | Efficient space k-kernel mining method for space data |
| CN113254797A (en) * | 2021-04-19 | 2021-08-13 | 江汉大学 | Searching method, device and processing equipment for social network community |
| CN113254797B (en) * | 2021-04-19 | 2022-09-20 | 江汉大学 | Searching method, device and processing equipment for social network community |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110119462B (en) | 2021-07-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110119462A (en) | A kind of community search method of net with attributes | |
| Gao et al. | Extracting urban functional regions from points of interest and human activities on location‐based social networks | |
| CN107391512B (en) | Method and device for predicting knowledge graph | |
| Yang et al. | Identifying points of interest by self-tuning clustering | |
| CN105701123A (en) | Passenger-vehicle relationship identification method and apparatus | |
| CN111291145B (en) | Mapping method, device and storage medium of wireless hotspot and interest point | |
| CN108799844B (en) | Fuzzy set-based water supply network pressure monitoring point site selection method | |
| CN110298687B (en) | Regional attraction assessment method and device | |
| Zhao et al. | Path $ k\hbox {NN} $ Query Processing in Mobile Systems | |
| CN104850489A (en) | Mobile application test system | |
| Cui et al. | Place your next branch with mile-run: Min-dist location selection over user movement | |
| CN117634704A (en) | Site selection optimization method, system, equipment and medium for tourist attractions | |
| Pilehforooshha et al. | A new model combining building block displacement and building block area reduction for resolving spatial conflicts | |
| Yu et al. | Ranking spatial units with structural property and traffic distributions for uncovering spatial interaction patterns in a city | |
| Wu et al. | Urban functional area recognition based on unbalanced clustering | |
| Shahabi et al. | Indexing land surface for efficient knn query | |
| Ghaemi et al. | A comparative study of two approaches for supporting optimal network location queries | |
| Xiao | Comparing and clustering residential layouts using a novel measure of grating difference | |
| Stadler et al. | A method for the optimized placement of bus stops based on voronoi diagrams | |
| Wang et al. | STME: An effective method for discovering spatiotemporal multi‐type clusters containing events with different densities | |
| CN110674843A (en) | Method and system for generating parking lot entity | |
| Alamri et al. | Vertical indexing for moving objects in multifloor environments | |
| CN111339446A (en) | Interest point mining method and device, electronic equipment and storage medium | |
| Xie et al. | A Graph Neural Network-Based Map Tiles Extraction Method Considering POIs Priority Visualization on Web Map Zoom Dimension | |
| Carreno et al. | Spatial electric load forecasting using an evolutionary heuristic |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CP03 | Change of name, title or address |
Address after: 310000 Room 501, building 9, No. 20, kekeyuan Road, Baiyang street, Hangzhou Economic and Technological Development Zone, Zhejiang Province Patentee after: Hangzhou Zhongke advanced technology development Co.,Ltd. Address before: 310000 Room 501, building 9, No. 20, Science Park Road, Baiyang street, economic and Technological Development Zone, Jianggan District, Hangzhou City, Zhejiang Province Patentee before: HANGZHOU ZHONGKE ADVANCED TECHNOLOGY RESEARCH INSTITUTE Co.,Ltd. |
|
| CP03 | Change of name, title or address |