CN111934938B - Flow network key node identification method and device based on multi-attribute information fusion - Google Patents

Flow network key node identification method and device based on multi-attribute information fusion Download PDF

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CN111934938B
CN111934938B CN202010958093.4A CN202010958093A CN111934938B CN 111934938 B CN111934938 B CN 111934938B CN 202010958093 A CN202010958093 A CN 202010958093A CN 111934938 B CN111934938 B CN 111934938B
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CN111934938A (en
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阮逸润
汤俊
白亮
郭金林
郭延明
何华
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Abstract

The application relates to a flow network key node identification method and device based on multi-attribute information fusion. The method comprises the following steps: constructing a node flow network according to the event to be identified; acquiring a maximum flow counting index, a minimum cut counting index and a maximum flow centrality index of each node in a node flow network; constructing an identification framework of node importance in a node flow network; in an identification frame, calculating a maximum flow counting index, a minimum cut counting index and a basic probability assignment function corresponding to a maximum flow centrality index; fusing basic probability assignment functions corresponding to the maximum flow counting index, the minimum cut counting index and the maximum flow centrality index to obtain an importance index of the node; and sequencing the nodes in the node flow network according to the importance indexes, and determining the key nodes in the node flow network according to the sequencing result. By adopting the method, the accuracy of key node identification can be improved.

Description

Flow network key node identification method and device based on multi-attribute information fusion
Technical Field
The present application relates to the field of streaming network technologies, and in particular, to a method and an apparatus for identifying a streaming network key node based on multi-attribute information fusion.
Background
In the real world, our lives are dominated by a large number of networks. The flow network may represent many models, such as oil in a pipe, current in a high voltage line, or data in a computer network. Network flow can also solve many problems, such as how to perform road traffic control, so as to effectively alleviate congestion of early peaks; in the transportation of a logistics network, the cost of a channel is minimized while the supply and demand relationship is met; how to make more serious strikes on enemy service lines while bombers are performing bombing missions.
Currently, the importance evaluation for nodes in a stream network is defined by edges connecting nodes, such as the degree of ingress and egress, the centrality, and the like. Identifying key nodes in the flow network by edges is not accurate, however.
Disclosure of Invention
Therefore, in order to solve the above technical problems, it is necessary to provide a method and an apparatus for identifying a key node of a stream network based on multi-attribute information fusion, which can solve the problem of inaccuracy of the key node in the stream network.
A flow network key node identification method based on multi-attribute information fusion comprises the following steps:
constructing a node flow network according to the event to be identified;
acquiring a maximum flow count index, a minimum cut count index and a maximum flow centrality index of each node in the node flow network;
constructing an identification framework of the importance of the nodes in the node flow network;
in the identification framework, respectively calculating basic probability assignment functions corresponding to the maximum flow count index, the minimum cut count index and the maximum flow centrality index;
fusing basic probability assignment functions corresponding to the maximum flow counting index, the minimum cut counting index and the maximum flow centrality index to obtain an importance index of a node;
and sequencing the nodes in the node flow network according to the importance indexes, and determining key nodes in the node flow network according to a sequencing result.
In one embodiment, the method further comprises the following steps: acquiring the maximum flow count index of each node in the node flow network as follows:
Figure 274369DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure 226145DEST_PATH_IMAGE002
representing a maximum flow count index of a node i in the nodal flow network, n representing a number of nodes in the nodal flow network, s representing a source node in the nodal flow network, t representing a maximum flow count index of a node i in the nodal flow network, n representing a number of nodes in the nodal flow network, s representing a source node in the nodal flow network, and t representing a maximum flow count index of a node i in the nodalV represents a set of nodes of the node flow network; when node i is in the maximum traffic path of the source node s and the sink node t,
Figure 964293DEST_PATH_IMAGE003
otherwise
Figure 608901DEST_PATH_IMAGE004
。
In one embodiment, the method further comprises the following steps: acquiring the minimum cut count index of each node in the node flow network as follows:
Figure 65291DEST_PATH_IMAGE005
wherein the content of the first and second substances,
Figure 305910DEST_PATH_IMAGE006
representing a minimal cut count index of a node i in the nodal stream network, n representing the number of nodes in the nodal stream network, s representing a source node in the nodal stream network, t representing a sink node in the nodal stream network, and V representing a set of nodes of the nodal stream network; when node i is the end point of the minimal cut set composed of node s and node t,
Figure 898565DEST_PATH_IMAGE007
and if not, the step (B),
Figure 448496DEST_PATH_IMAGE008
。
in one embodiment, the method further comprises the following steps: obtaining the maximum flow centrality index of each node in the node flow network as follows:
Figure 126602DEST_PATH_IMAGE009
wherein the content of the first and second substances,
Figure 420180DEST_PATH_IMAGE010
represents the maximumA flow centrality indicator, s represents a source node in the node flow network, t represents a sink node in the node flow network,
Figure 132921DEST_PATH_IMAGE011
representing the maximum flow from the source node s to the sink node t,
Figure 338905DEST_PATH_IMAGE012
to represent
Figure 504307DEST_PATH_IMAGE013
Sum of traffic flowing into node i and flowing out of node i.
A flow network key node identification device based on multi-attribute information fusion, the device comprising:
the network construction module is used for constructing a node flow network according to the event to be identified;
the index acquisition module is used for acquiring a maximum flow count index, a minimum cut count index and a maximum flow centrality index of each node in the node flow network;
the important index calculation module is used for constructing an identification framework of the importance of the nodes in the node flow network; in the identification framework, respectively calculating basic probability assignment functions corresponding to the maximum flow count index, the minimum cut count index and the maximum flow centrality index; fusing basic probability assignment functions corresponding to the maximum flow counting index, the minimum cut counting index and the maximum flow centrality index to obtain an importance index of a node;
and the key node identification module is used for sequencing the nodes in the node flow network according to the importance index and determining the key nodes in the node flow network according to the sequencing result.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
constructing a node flow network according to the event to be identified;
acquiring a maximum flow count index, a minimum cut count index and a maximum flow centrality index of each node in the node flow network;
constructing an identification framework of the importance of the nodes in the node flow network;
in the identification framework, respectively calculating basic probability assignment functions corresponding to the maximum flow count index, the minimum cut count index and the maximum flow centrality index;
fusing basic probability assignment functions corresponding to the maximum flow counting index, the minimum cut counting index and the maximum flow centrality index to obtain an importance index of a node;
and sequencing the nodes in the node flow network according to the importance indexes, and determining key nodes in the node flow network according to a sequencing result.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
constructing a node flow network according to the event to be identified;
acquiring a maximum flow count index, a minimum cut count index and a maximum flow centrality index of each node in the node flow network;
constructing an identification framework of the importance of the nodes in the node flow network;
in the identification framework, respectively calculating basic probability assignment functions corresponding to the maximum flow count index, the minimum cut count index and the maximum flow centrality index;
fusing basic probability assignment functions corresponding to the maximum flow counting index, the minimum cut counting index and the maximum flow centrality index to obtain an importance index of a node;
and sequencing the nodes in the node flow network according to the importance indexes, and determining key nodes in the node flow network according to a sequencing result.
According to the flow network key node identification method, the flow network key node identification device, the computer equipment and the storage medium based on multi-attribute information fusion, a node flow network is constructed, then the maximum flow count index, the minimum cut count index and the maximum flow centrality index of each node in the node flow network are extracted, and the basic probability assignment functions corresponding to the maximum flow count index, the minimum cut count index and the maximum flow centrality index are fused into the importance indexes of the nodes by combining a D-S evidence theory, so that node importance ranking is carried out according to the importance indexes. According to the scheme, the indexes are fused by extracting the indexes, so that the importance of the nodes can be better reflected.
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Fig. 1 is a schematic flowchart of a flow network key node identification method based on multi-attribute information fusion in an embodiment;
FIG. 2 is a block diagram of a flow network key node identification apparatus based on multi-attribute information fusion according to an embodiment;
FIG. 3 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In one embodiment, as shown in fig. 1, a method for identifying a flow network key node based on multi-attribute information fusion is provided, which includes the following steps:
and 102, constructing a node flow network according to the event to be identified.
The event to be identified can be an important network node identification event in network attack, or an identification event of an important position in traffic management, and a node flow network can be constructed according to the distribution and connection conditions of all nodes.
And 104, acquiring a maximum flow count index, a minimum cut count index and a maximum flow centrality index of each node in the node flow network.
It is worth to be noted that after the node flow network is obtained, the network maximum flow problem among the nodes needs to be calculated, and specifically, the problem can be solved through a linear programming model:
Figure 601576DEST_PATH_IMAGE014
wherein the content of the first and second substances,
Figure 168824DEST_PATH_IMAGE015
indicating a link
Figure 794977DEST_PATH_IMAGE016
The first formula represents the model solution objective, i.e. for any source nodesTo the sink nodetThe node pair(s) in (c), plans the maximum traffic as possible,
Figure 713255DEST_PATH_IMAGE017
and is
Figure 99368DEST_PATH_IMAGE018
Egress source nodesFlow and inflow sink nodedIs equal to the maximum flow between them. The second formula represents a node traffic balancing condition that ensures that traffic flowing into and out of the internal node is equal. The third formula represents the capacity limit of the link and the non-negativity of the traffic. Assuming that the maximum flows from each pair of source nodes to the sink nodes are independent from each other, the average is obtained after the maximum flows from each pair of source nodes to the sink nodes in the network are added, and the average is the network average maximum flow.
On the basis of the average maximum flow of the network, a maximum flow counting index, a minimum cut counting index and a maximum flow centrality index can be obtained.
And 106, constructing an identification framework of the importance of the nodes in the node flow network.
Based on the D-S evidence theory, the identification framework is defined as a recognition framework (frame of recognition). Suppose that
Figure 255543DEST_PATH_IMAGE019
Is formed bynA finite complete set of mutually exclusive elements is called
Figure 583756DEST_PATH_IMAGE020
To identify the frame. It is assumed that the importance of the nodes in the network is divided into two categories, namely important and unimportant. The definition of the identification frame established for each attribute of the node is as follows
Figure 458171DEST_PATH_IMAGE021
WhereinIIt is shown that the importance of the representation,Uthe representation is not important.
In this step, an identification frame of each attribute may be defined as
Figure 897243DEST_PATH_IMAGE022
Wherein h represents important and l represents unimportant.
And 108, respectively calculating basic probability assignment functions corresponding to the maximum flow counting index, the minimum cut counting index and the maximum flow centrality index in the identification frame.
The definition of the basic probability assignment function (BPA) is for a set
Figure 173503DEST_PATH_IMAGE023
Any subset ofASatisfy the following requirements
Figure 420420DEST_PATH_IMAGE024
，
Figure 782131DEST_PATH_IMAGE025
，
Figure 24894DEST_PATH_IMAGE026
Wherein
Figure 421240DEST_PATH_IMAGE027
Indicating an empty set.
Specifically, the extreme values of the node attributes can be used as reference values to construct a BPA conversion model, so that the attribute values of the nodes are converted into a BPA form.
And step 110, fusing basic probability assignment functions corresponding to the maximum flow counting index, the minimum cut counting index and the maximum flow centrality index to obtain an importance index of the node.
In this step, the attributes of the nodes can be fused based on the Dempster combination rule, and finally, a BPA comprehensive value synthesized by three indexes of MFNC, MCC and MLC of each node of the network is obtained. The BPA composite value is an importance indicator, the importance indicator is a set of values respectively representing the important, unimportant and important or unknown support degrees of the node, the important or unknown is averagely assigned to the important and unimportant, and the final composite attribute single value of the node is obtained by calculating the difference between the important support degree of the node and the unimportant support degree of the node.
And 112, sequencing the nodes in the node flow network according to the importance indexes, and determining key nodes in the node flow network according to the sequencing result.
In the flow network key node identification method based on multi-attribute information fusion, a node flow network is constructed, then the maximum flow count index, the minimum cut count index and the maximum flow centrality index of each node in the node flow network are extracted, and the basic probability assignment functions corresponding to the maximum flow count index, the minimum cut count index and the maximum flow centrality index are fused into the importance indexes of the nodes by combining a D-S evidence theory, so that node importance ranking is carried out according to the importance indexes. According to the scheme, the indexes are fused by extracting the indexes, so that the importance of the nodes can be better reflected.
In one embodiment, the maximum flow count index of each node in the node flow network is obtained as follows:
Figure 825677DEST_PATH_IMAGE028
wherein the content of the first and second substances,
Figure 690996DEST_PATH_IMAGE029
representing a maximum flow count index, n, for a node i in a node flow networkIndicating the number of nodes in the node flow network, s indicating a source node in the node flow network, t indicating a sink node in the node flow network, and V indicating a node set of the node flow network; when node i is in the maximum traffic path of the source node s and the sink node t,
Figure 206291DEST_PATH_IMAGE030
otherwise
Figure 457143DEST_PATH_IMAGE031
。
In this embodiment, the maximum flow count index is similar to the idea of the betweenness centrality index, and the difference is that the betweenness centrality index considers the maximum flow path rather than the shortest path. The core idea is that the more times the maximum flow path between all the source points and sinks of the network passes through a node, the more important the node is.
In one embodiment, the minimum cut count index of each node in the node flow network is obtained as follows:
Figure 298061DEST_PATH_IMAGE032
wherein the content of the first and second substances,
Figure 634364DEST_PATH_IMAGE033
representing a minimal cut count index for a node i in the nodal flow network, n representing a number of nodes in the nodal flow network, s representing a source node in the nodal flow network, t representing a sink node in the nodal flow network, V representing a set of nodes of the nodal flow network, when the node i is an endpoint of a minimal cut set formed by the node s and the node t,
Figure 953350DEST_PATH_IMAGE034
and if not, the step (B),
Figure 75021DEST_PATH_IMAGE035
。
in this embodiment, the cut is a partition of the fixed points in the network that divides all vertices in the network into two sets of verticesSAndTwherein the source node
Figure 821260DEST_PATH_IMAGE036
Sink node
Figure 644859DEST_PATH_IMAGE037
. According to the maximum flow and minimum cut theorem,s-tthe maximum flow is equal to its minimum cut. If the link is
Figure 298695DEST_PATH_IMAGE038
Is thats-tIs a member of the minimal cut set, it is the bottleneck link in the corresponding maximum traffic problem. If it is not
Figure 274872DEST_PATH_IMAGE038
Removed from the network, the traffic value of the maximum flow also becomes smaller. The node importance is equivalent to the number of times the node appears on the minimal cut set of all the source and sink points of the network.
In one embodiment, the maximum flow centrality index of each node in the node flow network is obtained as follows:
Figure 192012DEST_PATH_IMAGE039
wherein the content of the first and second substances,
Figure 768487DEST_PATH_IMAGE040
representing a maximum flow centrality index, s representing a source node in a nodal flow network, t representing a sink node in the nodal flow network,
Figure 429276DEST_PATH_IMAGE041
representing the maximum flow from the source node s to the sink node t,
Figure 243648DEST_PATH_IMAGE042
to represent
Figure 597269DEST_PATH_IMAGE043
Sum of traffic flowing into node i and flowing out of node i。
In this embodiment, similar to the flow betweenness centrality idea, a core idea of the maximum flow centrality index is that the larger a sum of flows of the maximum flow between all source points and sinks of the network passing through the node is, the more important the node is.
In one embodiment, the importance indicators of the nodes are obtained by fusing basic probability assignment functions corresponding to the maximum flow count indicator, the minimum cut count indicator, and the maximum flow centrality indicator, which may specifically be: the basic probability assignment functions corresponding to the maximum flow counting index, the minimum cut counting index and the maximum flow centrality index are 3 mutually independent basic probability assignment functions on the identification frame, and the Dempster combination rule is based on the fusion of the three, and the following formula can be adopted:
Figure 143263DEST_PATH_IMAGE044
Figure 607743DEST_PATH_IMAGE045
where n =3 and K is called the collision coefficient, and is used to measure the degree of dissimilarity between BPA functions. The larger K is, the larger the degree of dissimilarity is, and when K =1, the combination rule cannot be used. When K =0, it indicates that the degree of collision between BPA functions is minimal, i.e., the two do not collide.
It should be noted that, when node ranking is performed, ranking with a large importance index may be performed before, and the more the ranking before, the more critical the node is.
It should be understood that, although the steps in the flowchart of fig. 1 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in fig. 1 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 2, there is provided a flow network key node identification apparatus based on multi-attribute information fusion, including: a network construction module 202, an index acquisition module 204, an important index calculation module 206, and a key node identification module 208, wherein:
a network construction module 202, configured to construct a node flow network according to the event to be identified;
an index obtaining module 204, configured to obtain a maximum flow count index, a minimum cut count index, and a maximum flow centrality index of each node in the node flow network;
an importance index calculation module 206, configured to construct an identification framework of importance of nodes in the node flow network; in the identification framework, respectively calculating basic probability assignment functions corresponding to the maximum flow count index, the minimum cut count index and the maximum flow centrality index; fusing basic probability assignment functions corresponding to the maximum flow counting index, the minimum cut counting index and the maximum flow centrality index to obtain an importance index of a node;
and the key node identification module 208 is configured to rank the nodes in the node flow network according to the importance index, and determine the key nodes in the node flow network according to a ranking result.
In one embodiment, the metric obtaining module 204 is further configured to obtain a maximum flow count metric of each node in the node flow network as:
Figure 542201DEST_PATH_IMAGE046
wherein the content of the first and second substances,
Figure 66723DEST_PATH_IMAGE047
presentation instrumentA maximum flow count index of a node i in the node flow network, n represents the number of nodes in the node flow network, s represents a source node in the node flow network, t represents a sink node in the node flow network, and V represents a node set of the node flow network; when node i is in the maximum traffic path of the source node s and the sink node t,
Figure 86632DEST_PATH_IMAGE048
otherwise
Figure 354802DEST_PATH_IMAGE049
。
In one embodiment, the index obtaining module 204 is further configured to obtain a minimum cut count index of each node in the node flow network as:
Figure 894499DEST_PATH_IMAGE050
wherein the content of the first and second substances,
Figure 855502DEST_PATH_IMAGE051
representing a minimal cut count index of a node i in the nodal stream network, n representing the number of nodes in the nodal stream network, s representing a source node in the nodal stream network, t representing a sink node in the nodal stream network, and V representing a set of nodes of the nodal stream network; when node i is the end point of the minimal cut set composed of node s and node t,
Figure 362706DEST_PATH_IMAGE052
and if not, the step (B),
Figure 168988DEST_PATH_IMAGE053
。
in one embodiment, the index obtaining module 204 is further configured to obtain a maximum flow centrality index of each node in the node flow network as:
Figure 343618DEST_PATH_IMAGE054
wherein the content of the first and second substances,
Figure 695096DEST_PATH_IMAGE055
representing a maximum flow centrality indicator, s representing a source node in the nodal flow network, t representing a sink node in the nodal flow network,
Figure 955176DEST_PATH_IMAGE056
representing the maximum flow from the source node s to the sink node t,
Figure 299569DEST_PATH_IMAGE057
to represent
Figure 876175DEST_PATH_IMAGE058
Sum of traffic flowing into node i and flowing out of node i.
For specific definition of the flow network key node identification device based on multi-attribute information fusion, refer to the above definition of the flow network key node identification method based on multi-attribute information fusion, and are not described herein again. All or part of each module in the flow network key node identification device based on multi-attribute information fusion can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 3. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to realize a flow network key node identification method based on multi-attribute information fusion. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 3 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, a computer device is provided, comprising a memory storing a computer program and a processor implementing the steps of the method in the above embodiments when the processor executes the computer program.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the method in the above-mentioned embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.

Claims (4)

1. A flow network key node identification method based on multi-attribute information fusion is characterized by comprising the following steps:
constructing a node flow network according to the event to be identified;
acquiring a maximum flow count index, a minimum cut count index and a maximum flow centrality index of each node in the node flow network;
constructing an identification framework of the importance of the nodes in the node flow network;
in the identification framework, respectively calculating basic probability assignment functions corresponding to the maximum flow count index, the minimum cut count index and the maximum flow centrality index;
fusing basic probability assignment functions corresponding to the maximum flow counting index, the minimum cut counting index and the maximum flow centrality index to obtain an importance index of a node;
sorting the nodes in the node flow network according to the importance indexes, and determining key nodes in the node flow network according to sorting results;
acquiring the maximum flow count index of each node in the node flow network, including:
acquiring the maximum flow count index of each node in the node flow network as follows:
Figure 184645DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure 691850DEST_PATH_IMAGE002
representing a maximum flow count index of a node i in the node flow network, n representing the number of nodes in the node flow network, s representing a source node in the node flow network, t representing a sink node in the node flow network, and V representing a node set of the node flow network;
when node i is in the maximum traffic path of the source node s and the sink node t,
Figure 294869DEST_PATH_IMAGE003
otherwise
Figure 203920DEST_PATH_IMAGE004
；
Obtaining a minimum cut count index of each node in the node flow network, including:
acquiring the minimum cut count index of each node in the node flow network as follows:
Figure 742348DEST_PATH_IMAGE005
wherein the content of the first and second substances,
Figure 736849DEST_PATH_IMAGE006
representing a minimal cut count index of a node i in the nodal stream network, n representing the number of nodes in the nodal stream network, s representing a source node in the nodal stream network, t representing a sink node in the nodal stream network, and V representing a set of nodes of the nodal stream network;
when node i is the end point of the minimal cut set composed of node s and node t,
Figure 143560DEST_PATH_IMAGE007
and if not, the step (B),
Figure 641537DEST_PATH_IMAGE008
；
obtaining a maximum flow centrality index of each node in the node flow network, including:
obtaining the maximum flow centrality index of each node in the node flow network as follows:
Figure 882026DEST_PATH_IMAGE009
wherein the content of the first and second substances,
Figure 832664DEST_PATH_IMAGE010
representing a maximum flow centrality indicator, s representing a source node in the nodal flow network, t representing a sink node in the nodal flow network,
Figure 980749DEST_PATH_IMAGE011
representing the maximum flow from the source node s to the sink node t,
Figure 661129DEST_PATH_IMAGE012
to represent
Figure 869256DEST_PATH_IMAGE013
Sum of traffic flowing into node i and flowing out of node i.
2. A flow network key node identification device based on multi-attribute information fusion is characterized in that the device comprises:
the network construction module is used for constructing a node flow network according to the event to be identified;
the index acquisition module is used for acquiring a maximum flow count index, a minimum cut count index and a maximum flow centrality index of each node in the node flow network;
the important index calculation module is used for constructing an identification framework of the importance of the nodes in the node flow network; in the identification framework, respectively calculating basic probability assignment functions corresponding to the maximum flow count index, the minimum cut count index and the maximum flow centrality index; fusing basic probability assignment functions corresponding to the maximum flow counting index, the minimum cut counting index and the maximum flow centrality index to obtain an importance index of a node;
the key node identification module is used for sequencing the nodes in the node flow network according to the importance index and determining the key nodes in the node flow network according to the sequencing result;
the index obtaining module is further configured to obtain a maximum flow count index of each node in the node flow network as:
Figure 510453DEST_PATH_IMAGE014
wherein the content of the first and second substances,
Figure 196649DEST_PATH_IMAGE015
representing a maximum flow count index of a node i in the node flow network, n representing the number of nodes in the node flow network, s representing a source node in the node flow network, t representing a sink node in the node flow network, and V representing a node set of the node flow network; when node i is at the source nodeWhen in the maximum traffic path of point s and sink node t,
Figure 997115DEST_PATH_IMAGE016
otherwise
Figure 376144DEST_PATH_IMAGE017
；
The index obtaining module is further configured to obtain a minimum cut count index of each node in the node flow network as:
Figure 504637DEST_PATH_IMAGE018
wherein the content of the first and second substances,
Figure 994524DEST_PATH_IMAGE019
representing a minimal cut count index of a node i in the nodal stream network, n representing the number of nodes in the nodal stream network, s representing a source node in the nodal stream network, t representing a sink node in the nodal stream network, and V representing a set of nodes of the nodal stream network; when node i is the end point of the minimal cut set composed of node s and node t,
Figure 56021DEST_PATH_IMAGE020
and if not, the step (B),
Figure 668268DEST_PATH_IMAGE021
；
the index obtaining module is further configured to obtain a maximum flow centrality index of each node in the node flow network as:
Figure 346374DEST_PATH_IMAGE022
wherein the content of the first and second substances,
Figure 577635DEST_PATH_IMAGE023
to representA maximum flow centrality indicator, s represents a source node in the nodal flow network, t represents a sink node in the nodal flow network,
Figure 24797DEST_PATH_IMAGE024
representing the maximum flow from the source node s to the sink node t,
Figure 542366DEST_PATH_IMAGE025
to represent
Figure 707768DEST_PATH_IMAGE026
Sum of traffic flowing into node i and flowing out of node i.
3. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of claim 1 when executing the computer program.
4. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method as claimed in claim 1.
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