CN103729467B - Community structure discovery method in social network - Google Patents
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Abstract
The invention discloses a community structure discovery method in a social network, and belongs to the technical field of a network. The method comprises the steps of I. converting the social network into an adjacent matrix form, judging a corresponding edge as 1 if an edge exists between two nodes, otherwise judging to be 0; II. processing the adjacent matrix depending on a random walk theory to obtain a new node degree P-degree and an edge weight P-weight; III. obtaining a dominant node in the social network depending on the new node degree P-degree; and IV. generating a sub community depending on the dominant node, and implementing community discovery through a series of operations on the sub community. The method disclosed by the invention can effectively identify a community structure in the social network, and simultaneously compare the method with some classical community discovery algorithms such as Newman algorithm, and good performance is shown on a modularity index. The method disclosed by the invention has great significance in follow-up community network practice.
Description
Technical field
The invention belongs to networking technology area, it is related to the community structure discovery method in a kind of complexity social networks.
Background technology
In actual life, many complicated systems or occurred in the form of complex network, or complexity can be converted into
Network, such as social relation network, paper cooperative network, Computer Virus Spread network, facebook network, qq circle of friends etc.
Deng.Community discovery is exactly to detect and disclose intrinsic community structure in complex network.It is used to assist in it is appreciated that complicated
Function in network, finds the rule being hidden in complex network, and the behavior of prediction complex network.Since girvan and
Newman proposes gn algorithm so far, and the new method of new theory emerges in an endless stream.The application of community discovery related algorithm is not yet
Disconnected emerges in large numbers.
Except some classical community discovery algorithms, also have some algorithms can also obtain in community discovery reasonable
Divide effect, for example, Han Yi, Jia Yan et al. propose realize in community network community discovery method (patent No.:
201110103491.9, publication date: 2012.05.16)；What Lin Zhiting, Wu Xiulong et al. proposed is sent out based on the community of random walk
Existing method (patent No.: 201110177783.7, publication date: 2013.01.02)；A kind of society that Xu Bingying, Han Weihong et al. propose
Area finds method and system (patent No.: 201310201298.8, publication date: 2013.09.25) etc..In addition, Zhang Lu,
Cai Wandong et al. propose social network opinion leader identification method (patent No.: 201310028159.x, publication date:
2013.05.22)；Cai Lin, Cai Wandong et al. propose micro blog network opinion leader identification method (patent No.:
201310027808, publication date: 2013.06.05) etc. good elaboration has all been done to the identification of leader's node and effect, but
It is that identification with regard to leader's node yet has some shortcomings.
Based on some above-mentioned community discovery algorithms although corresponding community structure can be obtained, but made with modularity
When measurement for standard, still there are some shortcomings, the present invention is proposed and a kind of sent out based on the community of leader's node
Show algorithm it is intended to preferably obtain the community structure in social networks, especially in the case of using modularity as criterion,
Higher module angle value can be obtained, when the present invention is tested in actual classic network data set, the stable height of algorithm performance
Effect, algorithm is used in follow-up social network analysis having very important significance and wide application prospect.
Content of the invention
In view of this, it is an object of the invention to provide a kind of community structure discovery method in social networks, the method
It is to enter using the thought of random walk adjacency matrix is processed, obtain new node number of degrees p-degree and side right
Value-weight, can obtain the leader's node in social networks according to new node number of degrees p-degree, based on leader's node Lai
Generate sub- community, community discovery is carried out by the sequence of operations of antithetical phrase community.
For reaching above-mentioned purpose, the present invention following technical scheme of offer:
A kind of community structure discovery method in social networks, comprises the following steps: step one: social networks is converted to
, if there is side between two nodes in adjacency matrix form, then corresponding element is 1, otherwise for 0；Step 2: using with
Machine migration theory is processed to adjacency matrix, obtains new node number of degrees p-degree and side right value p-weight；Step
Three: the leader's node in social networks is obtained according to new node number of degrees p-degree；Step 4: son is generated based on leader's node
Community, and community discovery is carried out by the sequence of operations of antithetical phrase community.
Further, in step 2, using the corresponding adjacency matrix of random walk theoretical treatment social networks, will be new
The node number of degrees are named as p-degree, and the weights on new side are named as p-weight；The basis of leader's node is p-degree (i)
Value；According to original matrix a, obtain transition matrix p, its element representation is pij=aij/ki, wherein kiThe number of degrees for node i；With
When, p is obtained according to transition matrix pt, its element pij tGo to the general of node j for random walk person's from node i through t step
Rate；Matrix pf is used for representing the matrix pf=p* θ finally giving1+p2*θ2+p3*θ3......+pt*θt, Parameters in Formula θ1、θ2、
θ2......θt, 0≤θi≤ 1,1≤i≤t, represents and gives different weights to different transition matrixs；According to transition matrix pf
Obtain p-degree, (p-degree (i))=pf (i, i).
Further, in step 3, first the node in social networks is carried out descending row according to the value of p-degree (i)
Sequence, interstitial content is n, and using in drop down list, n/4 location element, as the threshold value of leader's node, is then carried out with this
The selection of leader's node；After confirming leader's node, the node being joined directly together with leader's node is closed using leader's node as core
And, preliminarily form sub- community structure.
Further, in step 4, the addition of remaining node and the place of sub- community lap are carried out using sequentially statistical model
Reason；Represent the weights on side using cos-similarity,
Wherein viAnd vj, i-th row of representing matrix pf and jth every trade vector；P-weight is worth to according to cos-similarity
The formula of (i, j) is as follows: (p-weight (i, j))=w* (cos-similarity), wherein w are weights；Antithetical phrase community ct, section
Point i is with respect to ctStatistical value beDifferent δ are calculated to different sub- communitiest, δ=max
(δ1, δ2δ3......δt), if δkMaximum, then node i just belongs to k-th sub- community.
Further, need less community is incorporated in larger community in step, processed using following steps:
Define little community concept, min_length=aver_length (a with min_lengths1,as2......ast)/4, wherein,
aver_length(as1,as2......ast) the Shi Ge community average nodal number that represents, for intercommunal merging, adopt
Below equation is carried out: ask=max_link(link(as1,ask),link(as2,ask)......link(ast,ask)), link
(ast,ask) that represent is ast,askThe number on the side connecting, tries to achieve askIt is connected that maximum community of side number with all of community
It is exactly will be with askThe community merging, obtains corresponding community structure after the completion of merging.
The beneficial effects of the present invention is: the community discovery method that the present invention provides, how more effective effectively solve
Discovery leader's node, and the leader's node that will be seen that be used for community discovery problem, can efficiently identify social networks
In community structure；Simultaneously by this method compared with the community discovery algorithm that some are classical such as newman algorithm, refer in modularity
Put on more preferable performance.The present invention is used in follow-up social networks practice having great significance.
Brief description
In order that the purpose of the present invention, technical scheme and beneficial effect are clearer, the present invention provides drawings described below to carry out
Illustrate:
Fig. 1 is the macro flow chart of the method for the invention；
Fig. 2 is applied to karate fight club network topology schematic diagram for this method；
Fig. 3 is applied to dolphins relational network topology schematic diagram for this method；
Fig. 4 is applied to americanfootball club network topology schematic diagram for this method.
Specific embodiment
Below in conjunction with accompanying drawing, the preferred embodiments of the present invention are described in detail.
The overall technology embodiment of the present invention is as follows:
1. test of heuristics data set
In the present embodiment, the data set being adopted has three, is karate club network respectively, dolpHins relation
Network and americanfootball network, its Network data set is described as follows:
1) karate fight club network
Phase early 1970s, wayne zachary observes karate club with the time of 2 years, and this family is empty-handed
Road club is a university from the U.S..Wayne zachary constructs the network of clubbite, and this network is root
Constituted according to the social relationships between member in club.But it is found that this club inside is asked in his fact-finding process
Topic is it is simply that about whether improving charge mark standard, between their supervisor and principal, suggestion creates difference.As a result, a part
Member is taken away by coach and has been organized into a new club, and remaining member then stays original club, finally
Zachary karate club splits into Liao Liangge little club, one headed by principal, another be then be responsible for as core
The heart.Shown in figure one is two different corporations that zachary karate club is divided into, and comprises 34 members and 78 altogether
Side, each node represents each member in the little club after division respectively.In the community structure of complex network is analyzed,
Zachary network has been widely applied in research network community structure partitioning algorithm, and what we used in the present invention is exactly
This data set.Algorithm in the present invention is applied to this data set by us, and the network topology structure obtaining is as shown in Figure 2.
2) dolphins relational network
At 1994 to calendar year 2001, the time that d.lusseau have studied 7 years to dolphin obtained dolphins network of personal connections
Network.This network comprises altogether 62 nodes, one dolphin of each of which node on behalf, and two dolphins have intimate relation,
A line is connected, the algorithm of the present invention is applied to this data set by us, obtains between the node just this two dolphins being represented
Network topology structure is as shown in Figure 3.
3) americanfootball network
One American university tissue 2000 season football connect the pool match of racing season.Wherein each of network section
Point represents a football team, and has match between the Shi Liangge team that the company side between node represents.And current all of ratio
Match is segmented into 12 groups, each team compare with the team's match number of times belonging to same a small group many, about 7；And with do not belong to
Relatively fewer in the match of a group, about 4, so a network with community structure has been formed by these teams.Logical
Cross our algorithm American university football match network is divided, a net with community structure has been formed by these teams
Network.The algorithm of the present invention is applied to this data set by us, and the network topology structure obtaining is as shown in Figure 4.
2. realize the community discovery algorithm based on leader's node
In order to find the leader's node in social networks and sub- community, first with random walk, adjacency matrix is carried out
Process, obtain the leader's node in community network, sub- community is built as core using leader's node, then by remaining node
Addition, and sub intercommunal merging obtains corresponding community structure.
Obtain leader's node according to the corresponding adjacency matrix of social networks to specifically comprise the following steps that
Step one: pretreatment is carried out by the adjacency matrix relative to social networks and obtains p-degree.According to original square
Battle array a, obtains transition matrix p, its element can be expressed as pij=aij/ki, wherein kiThe number of degrees for node i.Simultaneously, according to
Transition matrix p obtains pt, its element representation pij tRepresent that random walk person's from node i goes to the probability of node j through t step.
Matrix pf is used for representing the matrix pf=p* θ finally giving1+p2*θ2+p3*θ3......+pt*θt, Parameters in Formula θ1、θ2、
θ2......θt, 0≤θi≤ 1,1≤i≤t, represents and gives different weights to different transition matrixs.According to transition matrix pf
Obtain p-degree, (p-degree (i))=pf (i, i).
Step 2: obtain leader's node and sub- community using p-degree.First by the node in social networks according to
The value of p-degree (i) carries out descending sort, and interstitial content is n, and using in drop down list, n/4 location element is as leader
The threshold value of node, then carries out the selection of leader's node with this.After confirming leader's node, will be with as core using leader's node
The node that leader's node is joined directly together merges, and preliminarily forms sub- community structure.
After obtaining leader's node and correlator community, remaining work is exactly to obtain p-weight according to adjacency matrix
Value, the value according to p-weight adds to the remaining node in social networks, and the sub- community that finally will obtain is further
Merge and obtain final community structure.It specifically comprises the following steps that
Step one: the value of p-weight is obtained according to adjacency matrix and remaining node adds.We use cos-
Similarity representing the weights on side, Wherein viWith
vj, i-th row of representing matrix pf and jth every trade vector.The public affairs being worth to p-weight (i, j) according to cos-similarity
Formula is as follows: (p-weight (i, j))=w* (cos-similarity), wherein w are weights.Antithetical phrase community ct, node i is with respect to ct
Statistical value beDifferent δ are calculated to different sub- communitiest, δ=max (δ1, δ2δ3......
δt), if δkMaximum, then node i just belongs to k-th sub- community.
Step 2: sub- community merges further and obtains community structure.It is overlapping to there is multinode in the community structure obtaining now
Phenomenon, and the difference of intercommunal interstitial content sometimes can be very big, needs less community is incorporated in larger community,
Define little community concept, min_length=aver_length (a with min_lengths1,as2......ast)/4, aver_
length(as1,as2......ast) the Shi Ge community average nodal number that represents, as intercommunal merging, using following
Mathematical formulae ask=max_link(link(as1,ask),link(as2,ask)......link(ast,ask)), link (ast,ask)
That represent is ast,askThe number on the side connecting, tries to achieve askBeing connected that maximum community of side number with all of community is exactly will
With askThe community merging.Corresponding community structure is obtained after the completion of merging.
Finally illustrate, preferred embodiment above only in order to technical scheme to be described and unrestricted, although logical
Cross above preferred embodiment the present invention to be described in detail, it is to be understood by those skilled in the art that can be
In form and various changes are made to it, without departing from claims of the present invention limited range in details.
Claims (4)
1. the community structure discovery method in a kind of social networks it is characterised in that: comprise the following steps:
Step one: social networks is converted to adjacency matrix form, if there is side between two nodes, then corresponding unit
Element is 1, otherwise for 0；
Step 2: using random walk theory, adjacency matrix is processed, obtain new node number of degrees p-degree and side
Weights p-weight；
Step 3: the leader's node in social networks is obtained according to new node number of degrees p-degree；
Step 4: sub- community is generated based on leader's node, and community discovery is carried out by the sequence of operations of antithetical phrase community；
In step 2, using the corresponding adjacency matrix of random walk theoretical treatment social networks, by new node number of degrees life
Entitled p-degree, the weights on new side are named as p-weight；The basis of leader's node is the value of p-degree (i)；According to
Original matrix a, obtains transition matrix p, and its element representation is pij=aij/ki, wherein kiThe number of degrees for node i；Meanwhile, according to mistake
Cross matrix p and obtain pt, its element pij tGo to the probability of node j for random walk person's from node i through t step；Matrix pf uses
To represent the matrix pf=p* θ finally giving1+p2*θ2+p3*θ3......+pt*θt, Parameters in Formula θ1、θ2、θ2......θt, 0
≤θi≤ 1,1≤i≤t, represents and gives different weights to different transition matrixs；P- is obtained according to transition matrix pf
Degree, (p-degree (i))=pf (i, i).
2. the community structure discovery method in a kind of social networks according to claim 1 it is characterised in that: in step 3
In, first the node in social networks is carried out descending sort according to the value of p-degree (i), interstitial content is n, with descending chain
In table, n/4 location element, as the threshold value of leader's node, then carries out the selection of leader's node with this；Confirm leader
After node, as core, the node being joined directly together with leader's node is merged using leader's node, preliminarily form sub- community structure.
3. the community structure discovery method in a kind of social networks according to claim 2 it is characterised in that: in step 4
In, carry out the addition of remaining node and the process of sub- community lap using sequentially statistical model；Using cos-
Similarity representing the weights on side,Wherein viAnd vj, representing matrix
I-th row of pf and jth every trade vector；According to cos-similarity be worth to p-weight (i, j) formula as follows: (p-
Weight (i, j))=w* (cos-similarity), wherein w is weights；Antithetical phrase community ct, node i is with respect to ctStatistical value
ForDifferent δ are calculated to different sub- communitiest, δ=max (δ1, δ2δ3l l δt), if δkMaximum,
So node i just belongs to k-th sub- community.
4. the community structure discovery method in a kind of social networks according to claim 3 it is characterised in that: in step
Need less community is incorporated in larger community, processed using following steps: define little society with min_length
Area's concept, min_length=aver_length (as1,as2l l ast)/4, wherein, aver_length (as1,as2l l ast)
The Shi Ge community average nodal number representing, for intercommunal merging, is carried out using below equation: ask=max_link
(link(as1,ask),link(as2,ask)l l link(ast,ask)), link (ast,ask) that represent is ast,askThe side connecting
Number, try to achieve askBeing connected that maximum community of side number with all of community is exactly will be with askThe community merging, has merged
Corresponding community structure is obtained after one-tenth.
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| CN104657901B (en) * | 2015-01-14 | 2018-04-27 | 重庆邮电大学 | A kind of label based on random walk propagates community discovery method |
| CN104537126B (en) * | 2015-01-29 | 2017-12-01 | 中南大学 | A kind of overlapping community discovery method based on edge graph random walk |
| CN105095403A (en) * | 2015-07-08 | 2015-11-25 | 福州大学 | Parallel community discovery algorithm based on mixed neighbor message propagation |
| CN107993156B (en) * | 2017-11-28 | 2021-06-22 | 中山大学 | Social network directed graph-based community discovery method |
| CN108376371A (en) * | 2018-02-02 | 2018-08-07 | 众安信息技术服务有限公司 | A kind of internet insurance marketing method and system based on social networks |
| CN109828998B (en) * | 2019-01-14 | 2021-05-25 | 中国传媒大学 | Grouping method and system based on core group mining and opinion leader identification results |
| CN112269922B (en) * | 2020-10-14 | 2022-05-31 | 西华大学 | Community public opinion key character discovery method based on network representation learning |
| CN113723583B (en) * | 2021-08-28 | 2023-07-21 | 重庆理工大学 | Multi-domain network community discovery method based on discrete time quantum migration |
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