WO2020078370A1

WO2020078370A1 - Community search method

Info

Publication number: WO2020078370A1
Application number: PCT/CN2019/111419
Authority: WO
Inventors: 王朝坤; 竺俊超
Original assignee: 清华大学
Priority date: 2018-10-16
Filing date: 2019-10-16
Publication date: 2020-04-23
Also published as: CN109543077B; CN109543077A

Abstract

A community search method comprises: associating a node with a node variable according to a community search requirement of a user, and preparing a corresponding search condition (10); converting the search condition into multiple search items (20); performing a community search for each search item according to a single condition (30); and combining community search results for each single condition, acquiring a combination of the community results, and returning the combination thereof (40). In the method, all search conditions are converted into Boolean expressions, such that a user can easily customize the expression of a search requirement and can easily perform community searches with complex conditions. The method takes into account a community node that a user wishes to exclude, thereby further meeting user needs. Since a community is allowed to comprise at least one of given nodes, one search condition may result in multiple different community results meeting the search condition, providing the user with more options when selecting results. Multiple different implementation methods are provided, and can be selected according to actual requirements.

Description

Community search method

Technical field

The present disclosure relates to the field of search technology, for example, to a community search method.

Background technique

The network structure formed by a large number of nodes and the connection relationship between nodes widely exists in the fields of computer science, biology, sociology and so on. In network-related research work, the community has received constant attention from people. A community generally refers to a subgraph in which connections between internal nodes are closer than those between internal and external communities. The community structure excavated from the network helps people to recommend friends, identify criminal groups and predict protein functions. Community search (local community discovery) refers to given one or more nodes to find the community that contains them. Compared to global community discovery, it pays more attention to the local network structure and returns more personalized community results.

The current community search method is mainly based on k-clique, k-core, k-truss and other specific topological structures. In addition, there are some community search methods that comprehensively consider the topology and node attributes.

The community found based on the method of k-truss structure needs to satisfy the following properties: 1. The number of triangles on each side is not less than k-2; 2. Any two sides can be reached by a series of adjacent triangles. A typical method is to record the truss values of all adjacent edges around each node, and then organize the adjacent points of each node into a tree structure index according to the truss value of the edge, called TCP-index, and finally according to the given Node and k value, constantly find the neighbor nodes that can be expanded from the index, until it can not be expanded, you get a k-truss community containing the given node.

A typical method that comprehensively considers the topology structure and node attributes, such as the AGAR method, is to first supplement the edges of the original graph according to the attribute similarity between the nodes, thereby constructing a TA-graph, and then based on the k-truss structure on the TA-graph Conduct a community search and finally get the community containing the given node.

The disadvantage of current community search methods is that they can only find communities that contain a given set of nodes. When we actually conduct community search, we often encounter such needs: 1. The community must not only include certain given points, but also not allowed to include other given points; 2. The community must contain at least any one of the given nodes . Existing community search methods cannot meet the above needs. Therefore, the existing technology needs improvement.

The above background content is only used to help understand the present disclosure, and does not mean to acknowledge or endorse that any content mentioned is part of the common general knowledge relative to the present disclosure.

Summary of the invention

A technical problem to be solved by the embodiments of the present disclosure is to provide a community search method to solve the problems in the prior art. The community search method includes:

According to the user's needs for community search, the nodes are mapped to node variables, and the corresponding search conditions are written;

Convert search terms into multiple search terms;

A single-item community search is performed for each search term;

Combine the results of each single-item community search, that is, take the union result of the community and return it.

In another embodiment of the above community search method based on the present disclosure, the node is mapped to a node variable according to the user's demand for community search, and writing out the corresponding search conditions includes:

Corresponding to the nodes mentioned in user requirements as Boolean variables;

The nodes that appear in the community are not allowed to be logically undecorated, and the nodes that the community must contain are not decorated;

The nodes that must appear in the community at the same time are connected with logic and the nodes that the community must contain and the nodes that are not allowed are also connected with logic and;

The community must contain at least one of several nodes that need to be represented by logical or connected nodes.

In another embodiment of the above community search method based on the present disclosure, the conversion of the search condition into multiple search terms includes:

Enumerate the value combinations of node variables that satisfy the search condition, so as to obtain the main disjunctive paradigm equivalent to the search condition;

The main disjunctive paradigm is reduced to the simplest OR by the Quine-McCluskey algorithm;

Set each conjunct of the simplest and or as the search term;

Extract the most frequent occurrence of a variable without logical undecoration among different conjuncts. If there are more than one conjunct with this variable, merge these conjuncts into a new search term, and repeat this step until there is no conjunct The items can be merged. .

In another embodiment based on the above community search method of the present disclosure, the performing a single-item community search for each search item includes:

For search terms in conjunctive form, the nodes that must be included in the community and the nodes that are not allowed to be included are organized into the necessary node set and the prohibited node set as the input of the single-condition community search process;

For search terms resulting from the combination of multiple conjunctions, the extracted one or more common node variables are sorted into necessary node sets and prohibited node sets according to whether they can appear in the community as the input of the single-item community search process, the remaining Partly used to judge the output results;

Carry out a single conditional community search, using the necessary node set and the prohibited node set to search the community results from the network graph, so that the resulting community contains the necessary node set, and does not include the nodes in the prohibited node set.

In another embodiment of the above community search method based on the present disclosure, the single-item community search is performed, using the necessary node set and the prohibited node set to search the community results from the network graph, so that the resulting community contains the necessary node set, while not including The nodes in the prohibited node set include three implementation methods, namely: community search after filtering, weighted filtering, and filtering while searching.

In another embodiment of the above community search method based on the present disclosure, the filtered community search method includes:

Delete the forbidden node set from the network graph to get the network graph without prohibited nodes;

For the new network graph, use the necessary node set as input to conduct a community search.

In another embodiment of the above community search method based on the present disclosure, the weighted filtering method includes:

Assign numerical weights to all nodes in the network graph, making the necessary nodes be 1, prohibiting nodes being -1, and remaining nodes being 0;

In addition to the necessary and prohibited nodes, iteratively updates the weight of each node and assigns it to the average of the weights of all neighbor nodes, namely:

Set the node weight threshold λ, keep the nodes whose node weight is greater than or equal to λ, and extract the derived subgraph in the original network graph as a new network graph;

a01, the necessary node set is classified into the community result C;

a02, according to the given network graph, get the derived subgraph corresponding to the community result. If the derived subgraph of the community result C has only one connected component and all the nodes in the derived subgraph have node degrees greater than or equal to the given threshold k, Then stop and return to the community results;

a03, according to the connected components of the derived subgraph, the nodes in the same component are grouped into the same group;

a04, the neighbor nodes of all nodes in the community result C are classified into the candidate node set Candidate, and the nodes that already exist in the community result C are excluded;

a05, if Candidate is empty, set the community result C to an empty set, and go to step a08;

a06, for each node in the candidate node set Candidate, record the number a of the connected connected components, the number b of the nodes in the community result C, the degree d of the node in the given network graph, and then according to a , B and d sort the nodes in the node set in multi-key descending order;

a07, if the degree of the top candidate node c is less than the threshold k, then remove the node c from the candidate node set Candidate, go to step a06, otherwise, add node c to the community result, and join the node's neighbors Go to the candidate node set Candidate, and remove the node c from the candidate node set Candidate, go to step a02;

a08, the nodes of the entire network graph are included in the community result C;

a09, if the number of connected components of the derived subgraph of the community result C is greater than 1, stop and return to the empty set, if the degree of the smallest point in the derived subgraph of the community result C is greater than or equal to the threshold k, stop and return the community result C ;

a10. Delete the node whose degree is lower than k in the derived subgraph of the community result C from the community result C. If the deleted node is a member of the necessary node set, stop and return to the empty set, otherwise go to step a09.

In another embodiment of the above community search method based on the present disclosure, the performing community search on the new network graph with the necessary node set as input includes:

b01, the necessary node set is classified into community result C;

b02, according to the given network graph, get the derived subgraph corresponding to the community result. If the derived subgraph of the community result C has only one connected component and all the nodes in the derived subgraph have node degrees greater than or equal to the given threshold k, Then stop and return to the community results;

b03, according to the connected components of the derived subgraph, the nodes in the same component are grouped into the same group;

b04, the neighbor nodes of all nodes in the community result C are classified into the candidate node set Candidate, and the nodes that already exist in the community result C are excluded;

b05, if Candidate is empty, set the community result C to an empty set, and go to step b08;

b06, for each node in the candidate node set Candidate, record the number a of the connected connected components, the number b of the nodes in the community result C, the degree d of the node in the given network graph, and then according to the The nodes in the node set are sorted in multi-key descending order;

b07, if the degree of the top candidate node c is less than the threshold k, then remove the node c from the candidate node set Candidate, go to step b06, otherwise, add node c to the community result, and join the node's neighbors Go to the candidate node set Candidate, and remove the node c from the candidate node set Candidate, go to step b02;

b08, the nodes of the entire network graph are included in the community result C;

b09, if the number of connected components of the derived subgraph of the community result C is greater than 1, stop and return to the empty set, if the degree of the smallest point in the derived subgraph of the community result C is greater than or equal to the threshold k, stop and return to the community result C ;

b10. Delete the node whose degree is lower than k in the derived subgraph of community result C from community result C. If the deleted node is a member of the necessary node set, stop and return to the empty set, otherwise go to step b09.

In another embodiment based on the above community search method of the present disclosure, the manner of searching while filtering includes:

c01, the necessary node set is classified into community result C;

c02. Obtain the derived subgraph corresponding to the community result C according to the given network graph. If the derived subgraph of the community result C has only one connected component and the degree of all nodes in the derived subgraph is greater than or equal to the given threshold k, then stop and return to the community result C;

c03, according to the connected components of the derived subgraph, the nodes in the same component are grouped into the same group;

c04, the neighbor nodes of all nodes in the community result C are classified into the candidate node set Candidate, and the nodes and banned nodes that already exist in the community result C are excluded from it;

c05, if Candidate is empty, set the community result C to an empty set, and go to step c08;

c06, for each node in the candidate node set Candidate, record the number a of the connected different connected components, the number b of the nodes in the connection community result C, the number of nodes connected to non-prohibited nodes in the given network graph d; Sort the nodes in the node set in multi-key descending order according to a, b, and d;

c07, if the degree of the top candidate node c is less than the threshold k, then remove the node c from the candidate node set Candidate, go to step c06, otherwise, add the node c to the community result C, and at the same time the neighbor node of the node Add to candidate node set Candidate, and remove node c from candidate node set Candidate, go to step c02;

c08, classify the nodes of the entire network graph into the community result C and delete the prohibited nodes;

c09, if the number of connected components of the derived subgraph of the community result C is greater than 1, stop and return to the empty set. If the degree of the smallest point in the derived subgraph of the community result C is greater than or equal to the threshold k, check the community result C at this time Whether it contains prohibited nodes;

c10. Delete the node whose degree is lower than k in the derived subgraph of the community result C from the community result C. If the deleted node is a member of the necessary node set, stop and return to the empty set, otherwise go to step c09.

Compared with the prior art, the present disclosure has the following advantages:

The present disclosure proposes a community search method, which expresses the search conditions in the form of a unified Boolean expression, which is convenient for users to express their search needs personally, and also facilitates the execution of community search under complex conditions; due to the consideration in the process of community search In addition, users do not want to appear in the community, and the community results obtained by the search are more in line with the user's expectations, making the community results more personalized; because it is allowed to consider that the community contains at least one such demand in a given node, a search condition may Obtain multiple different community results, and all meet the search conditions, which makes the user's choice of results more abundant; provides a variety of different implementation methods, you can choose according to actual needs.

BRIEF DESCRIPTION

The drawings that form a part of the specification describe embodiments of the present disclosure, and together with the description serve to explain the principles of the present disclosure.

With reference to the drawings, the present disclosure can be more clearly understood from the following detailed description, in which:

FIG. 1 is a flowchart of an embodiment of a community search method of the present disclosure;

2 is a flowchart of another embodiment of the community search method of the present disclosure;

3 is a flowchart of another embodiment of the community search method of the present disclosure;

4 is a flowchart of yet another embodiment of the community search method of the present disclosure;

5 is a flowchart of another embodiment of the community search method of the present disclosure;

6 is a flowchart of yet another embodiment of the community search method of the present disclosure;

7 is a flowchart of yet another embodiment of the community search method of the present disclosure;

FIG. 8 is a flowchart of another embodiment of the community search method of the present disclosure.

detailed description

Various exemplary embodiments of the present disclosure will now be described in detail with reference to the drawings. It should be noted that the relative arrangement of components and steps, numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.

Techniques, methods and equipment known to those of ordinary skill in the related art may not be discussed in detail, but where appropriate, the techniques, methods and equipment should be considered as part of the specification.

FIG. 1 is a flowchart of an embodiment of a community search method of the present disclosure. As shown in FIG. 1, the community search method includes:

10. According to the user's needs for community search, correspond the node to the node variable, and write the corresponding search conditions. The node variable is also called Boolean variable, and the search condition is expressed by Boolean expression;

20. Convert the search criteria into multiple search terms;

30. For each search term, conduct a single-item community search;

40. Combine the results of each single-item community search, that is, take the union result of the community results and return it.

FIG. 2 is a flowchart of another embodiment of the community search method of the present disclosure. As shown in FIG. 2, according to the user ’s demand for community search, the nodes are mapped to node variables, and the corresponding search conditions are written out as follows:

101. Corresponding to the node mentioned in the user's needs as a Boolean variable;

102, the nodes that appear in the community are not allowed to be logically undecorated, and the nodes that the community must contain are undecorated. The symbol of the logical negation is

103, the nodes that must appear in the community at the same time are connected by logic and, the nodes that the community must contain and the nodes that are not allowed are also connected by logic and, the symbol of the logic and is "∧", for example: Boolean formula

Indicates that the user wants the community to include node A and node B, and node C is not allowed. ;

104. The community must contain at least one of several nodes that need to be represented by logical or connected nodes. The logical OR symbol is "∨", for example: the Boolean expression A∨B∨C indicates that the user wants the community to at least Contains one of node A, node B, and node C.

FIG. 3 is a flowchart of yet another embodiment of the community search method of the present disclosure. As shown in FIG. 3, the conversion of search conditions into multiple search terms includes:

201. Enumerate the value combinations of node variables that satisfy the search condition, so as to obtain a main disjunctive paradigm equivalent to the search condition;

202, the main disjunctive paradigm is reduced to the simplest OR by the Quine-McCluskey algorithm;

203. Set each conjunction of the simplest and OR formula as the search term, for example: the simplest and OR formula (A∧B) ∨ (C∧D) contains two search terms, which are (A∧B ) And (C∧D), if you find that there are several search terms with the same node variable, you can merge these search terms into a new search term, for example: the most simple and or

Search terms (A∧B) and

Contains the same node variable A, so you can extract the common node variable A, and merge these two conjunctions into

In order to reduce the number of search terms, thereby reducing the number of subsequent single-item community search processes, to achieve the purpose of saving time overhead;

204. Extract a variable with no logical undecoration that occurs most frequently among different conjuncts. If the conjuncts containing the variable are more than 1, then merge these conjuncts into a new search term and repeat this step until No conjuncts can be merged.

FIG. 4 is a flowchart of yet another embodiment of the community search method of the present disclosure. As shown in FIG. 4, performing a single-item community search for each search item includes:

301. For search terms in conjunctive form, the nodes that must be included in the community and the nodes that are not allowed to be included are organized into the necessary node set and the prohibited node set as the input of the single-item community search process, because it only contains the community Must appear in the node and nodes that are not allowed to appear;

302. For the search terms resulting from the combination of multiple conjunctions, the extracted one or more common node variables are organized into a necessary node set and a prohibited node set according to whether they can appear in the community as input to the single-item community search process , The remaining part is used to distinguish the output results, for example: in two search terms

Combined search terms

In, the necessary node set {A} is used as the input to the single-item community search process, that is, to find the community containing node A,

The discriminant as the output result is used to determine whether the community result contains node B or does not contain node D;

303. Perform a single conditional community search, and use the necessary node set and the prohibited node set to search the community results from the network graph, so that the resulting community contains the necessary node set, and does not include the nodes in the prohibited node set.

The single conditional community search described above uses the necessary node set and the prohibited node set to search the community results from the network graph, so that the resulting community contains the necessary node set, and the nodes that do not contain the prohibited node set include three implementation methods, namely: filtering Post-community search, weighted filtering, and filtering while searching.

The filtered community search method includes:

FIG. 5 is a flowchart of another embodiment of the community search method of the present disclosure. As shown in FIG. 5, the weighted filtering method includes:

401, assign numerical weights to all nodes in the network graph, make the necessary nodes to 1, prohibit nodes to be -1, and other nodes to be 0, namely:

402. In addition to the necessary and prohibited nodes, iteratively updates the weight of each node and assigns it to the average of the weights of all neighboring nodes, namely:

403. Set a node weight threshold λ, retain nodes whose node weight is greater than or equal to λ, and extract their derived subgraph in the original network graph as a new network graph;

404. Perform community search on the new network graph with the necessary node set as input.

FIG. 6 is a flowchart of another embodiment of the community search method of the present disclosure. As shown in FIG. 6, performing community search using the necessary node set as input for the new network diagram includes:

501, the necessary node set is classified into the community result C;

502. Obtain the derived subgraph corresponding to the community result according to the given network graph. If the derived subgraph of the community result C has only one connected component and the node degree of all nodes in the derived subgraph is greater than or equal to the given threshold k, Then stop and return to the community results;

503, according to the connected components of the derived subgraph, divide the nodes in the same component into the same group;

504, classify the neighbor nodes of all nodes in the community result C into the candidate node set Candidate, and exclude nodes that already exist in the community result C;

505, if Candidate is empty, set the community result C to an empty set, and go to step 508;

506. For each node in the candidate node set Candidate, record the number a of the connected connected components, the number b of the nodes in the community result C, the degree d of the node in the given network graph, and then according to a , B and d sort the nodes in the node set in multi-key descending order;

507, if the degree of the candidate node c ranked first is less than the threshold k, then the node c is removed from the candidate node set Candidate, go to step 506, otherwise, the node c is added to the community result, and the neighbor nodes of the node Go to the candidate node set Candidate, and remove the node c from the candidate node set Candidate, go to step 502;

508, the nodes of the entire network graph are classified into the community result C;

509, if the number of connected components of the derived subgraph of the community result C is greater than 1, stop and return to the empty set, if the degree of the smallest point in the derived subgraph of the community result C is greater than or equal to the threshold k, stop and return to the community result C ;

510. Delete the node whose degree is lower than k in the derived subgraph of the community result C from the community result C. If the deleted node is a member of the necessary node set, stop and return to the empty set, otherwise go to step 509.

FIG. 7 is a flowchart of another embodiment of the community search method of the present disclosure. As shown in FIG. 7, the performing community search on the new network graph using the necessary node set as input includes:

601, the necessary node set is classified into the community result C;

602. Obtain the derived subgraph corresponding to the community result according to the given network graph. If the derived subgraph of the community result C has only one connected component and the degree of all nodes in the derived subgraph is greater than or equal to the given threshold k, Then stop and return to the community results;

603, according to the connected components of the derived subgraph, the nodes in the same component are grouped into the same group;

604, classify the neighbor nodes of all nodes in the community result C into the candidate node set Candidate, and exclude nodes that already exist in the community result C;

605, if Candidate, the community result C is set to an empty set, and go to step 608;

606, for each node in the candidate node set Candidate, record the number a of the connected connected components, the number b of the nodes in the community result C, the degree d of the node in the given network graph, and then according to a , B and d sort the nodes in the node set in multi-key descending order;

607, if the degree of the candidate node c ranked first is less than the threshold k, the node c is removed from the candidate node set Candidate, go to step 606, otherwise, the node c is added to the community result, and the neighbor node of the node is also added Go to the candidate node set Candidate, and remove the node c from the candidate node set Candidate, go to step 602;

608, the nodes of the entire network graph are classified into the community result C;

609, if the number of connected components of the derived subgraph of the community result C is greater than 1, stop and return to the empty set, if the degree of the smallest point in the derived subgraph of the community result C is greater than or equal to the threshold k, stop and return to the community result C ;

610. Delete the node whose degree is lower than k in the derived subgraph of the community result C from the community result C. If the deleted node is a member of the necessary node set, stop and return to the empty set, otherwise go to step 609.

FIG. 8 is a flowchart of another embodiment of the community search method of the present disclosure. As shown in FIG. 8, the manner of searching while filtering includes:

701, the necessary node set is classified into the community result C;

702. Obtain the derived subgraph corresponding to the community result C according to the given network graph. If the derived subgraph of the community result C has only one connected component and the degree of all nodes in the derived subgraph is greater than or equal to the given threshold k, then stop and return to the community result C;

703, according to the connected components of the derived subgraph, the nodes in the same component are grouped into the same group;

704, classify the neighbor nodes of all nodes in the community result C into the candidate node set Candidate, and exclude the nodes and prohibited nodes that already exist in the community result C from it;

705, if Candidate is empty, the community result C is set to an empty set, and go to step 708;

706, for each node in the candidate node set Candidate, record the number a of its connected components, the number b of nodes in the community result C, and the number of nodes connected to non-prohibited nodes in the given network graph d; Sort the nodes in the node set in multi-key descending order according to a, b, and d;

707, if the degree of the candidate node c ranked first is less than the threshold k, then the node c is removed from the candidate node set Candidate, go to step 706, otherwise, the node c is added to the community result C, and the neighbor node of the node Add to candidate node set Candidate, and remove node c from candidate node set Candidate;

708, the nodes of the entire network graph are classified into the community result C and the prohibited nodes are deleted;

709, if the number of connected components of the derived subgraph of the community result C is greater than 1, then stop and return to the empty set, if the degree of the smallest point in the derived subgraph of the community result C is greater than or equal to the threshold k, then stop and return to the community result C ;

710. Delete the node whose degree is lower than k in the derived subgraph of the community result C. If the deleted node is a member of the necessary node set, stop and return to the empty set, otherwise jump to step 709.

Each embodiment in this specification is described in a progressive manner. Each embodiment focuses on the differences from other embodiments, and the same or similar parts between the various embodiments may refer to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and the relevant part can be referred to the description of the method embodiment.

The description of the present disclosure is given for the sake of example and description, and is not exhaustive or limits the present disclosure to the disclosed form. Many modifications and changes will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better explain the principles and practical applications of the present disclosure, and enable those of ordinary skill in the art to understand the present disclosure to design various embodiments with various modifications suitable for specific uses.

Claims

A method of community search, which includes:

According to the user's needs for community search, the nodes are mapped to node variables, and the corresponding search conditions are written;

Convert search terms into multiple search terms;

A single-item community search is performed for each search term;

Combine the results of each single-item community search, that is, take the union result of the community and return it.
The community search method according to claim 1, wherein, according to the user's demand for community search, the node is corresponded to a node variable, and writing out the corresponding search condition includes:

Corresponding to the nodes mentioned in user requirements as Boolean variables;

The nodes that appear in the community are not allowed to be logically undecorated, and the nodes that the community must contain are not decorated;

The nodes that must appear in the community at the same time are connected with logic and the nodes that the community must contain and the nodes that are not allowed are also connected with logic and;

The community must contain at least one of several nodes that need to be represented by logical or connected nodes.
The community search method according to claim 2, wherein the conversion of the search condition into multiple search terms includes:

Enumerate the value combinations of node variables that satisfy the search condition, so as to obtain the main disjunctive paradigm equivalent to the search condition;

The main disjunctive paradigm is reduced to the simplest OR by the Quine-McCluskey algorithm;

Set each conjunct of the simplest and or as the search term;

Extract the most frequent occurrence of a variable without logical undecoration among different conjuncts. If there are more than one conjunct with this variable, merge these conjuncts into a new search term, and repeat this step until there is no conjunct The items can be merged.
The community search method according to claim 3, wherein the community search with a single condition for each search item includes:

For search terms in conjunctive form, the nodes that must be included in the community and the nodes that are not allowed to be included are organized into the necessary node set and the prohibited node set as the input of the single-condition community search process;

For search terms resulting from the combination of multiple conjunctions, the extracted one or more common node variables are sorted into necessary node sets and prohibited node sets according to whether they can appear in the community as the input of the single-item community search process, the remaining Partly used to judge the output results;

Carry out a single conditional community search, using the necessary node set and the prohibited node set to search the community results from the network graph, so that the resulting community contains the necessary node set, and does not include the nodes in the prohibited node set.
The community search method according to claim 4, characterized in that the single-item community search is performed to search the community results from the network graph using the necessary node set and the prohibited node set, so that the resulting community contains the necessary node set and does not include The nodes in the prohibited node set include three implementation methods, namely: community search after filtering, weighted filtering, and filtering while searching.
The community search method according to claim 5, wherein the filtered community search method includes:

Delete the forbidden node set from the network graph to get the network graph without prohibited nodes;

For the new network graph, use the necessary node set as input to conduct a community search.
The community search method according to claim 5, wherein the weighted filtering method includes:

Assign numerical weights to all nodes in the network graph, making the necessary nodes be 1, prohibiting nodes being -1, and remaining nodes being 0;

In addition to the necessary and prohibited nodes, iteratively updates the weight of each node and assigns it to the average of the weights of all neighbor nodes, namely:

Set the node weight threshold λ, keep the nodes whose node weight is greater than or equal to λ, and extract the derived subgraph in the original network graph as a new network graph;

Use the necessary node set as input to conduct a community search on the new network graph.
The community search method according to claim 6, wherein the performing community search on the new network graph using the necessary node set as input includes:

a01, the necessary node set is classified into the community result C;

a02, according to the given network graph, get the derived subgraph corresponding to the community result. If the derived subgraph of the community result C has only one connected component and all the nodes in the derived subgraph have node degrees greater than or equal to the given threshold k, Then stop and return to the community results;

a03, according to the connected components of the derived subgraph, the nodes in the same component are grouped into the same group;

a04, the neighbor nodes of all nodes in the community result C are classified into the candidate node set Candidate, and the nodes that already exist in the community result C are excluded;

a05, if Candidate is empty, set the community result C to an empty set, and go to step a08;

a06, for each node in the candidate node set Candidate, record the number a of the connected connected components, the number b of the nodes in the community result C, the degree d of the node in the given network graph, and then according to a , B and d sort the nodes in the node set in multi-key descending order;

a07, if the degree of the top candidate node c is less than the threshold k, then remove the node c from the candidate node set Candidate, go to step a06, otherwise, add node c to the community result, and join the node's neighbors Go to the candidate node set Candidate, and remove the node c from the candidate node set Candidate, go to step a02;

a08, the nodes of the entire network graph are included in the community result C;

a09, if the number of connected components of the derived subgraph of the community result C is greater than 1, stop and return to the empty set, if the degree of the smallest point in the derived subgraph of the community result C is greater than or equal to the threshold k, stop and return the community result C ;

a10. Delete the node whose degree is lower than k in the derived subgraph of the community result C from the community result C. If the deleted node is a member of the necessary node set, stop and return to the empty set, otherwise go to step a09.
The community search method according to claim 7, wherein the performing community search on the new network graph with the necessary node set as input includes:

b01, the necessary node set is classified into community result C;

b02, according to the given network graph, get the derived subgraph corresponding to the community result. If the derived subgraph of the community result C has only one connected component and all the nodes in the derived subgraph have node degrees greater than or equal to the given threshold k, Then stop and return to the community results;

b03, according to the connected components of the derived subgraph, the nodes in the same component are grouped into the same group;

b04, the neighbor nodes of all nodes in the community result C are classified into the candidate node set Candidate, and the nodes that already exist in the community result C are excluded;

b05, if Candidate is empty, set the community result C to an empty set, and go to step b08;

b06, for each node in the candidate node set Candidate, record the number a of the connected connected components, the number b of the nodes in the community result C, the degree d of the node in the given network graph, and then according to the The nodes in the node set are sorted in multi-key descending order;

b07, if the degree of the top candidate node c is less than the threshold k, then remove the node c from the candidate node set Candidate, go to step b06, otherwise, add node c to the community result, and join the node's neighbors Go to the candidate node set Candidate, and remove the node c from the candidate node set Candidate, go to step b02;

b08, the nodes of the entire network graph are included in the community result C;

b09, if the number of connected components of the derived subgraph of the community result C is greater than 1, stop and return to the empty set, if the degree of the smallest point in the derived subgraph of the community result C is greater than or equal to the threshold k, stop and return to the community result C ;

b10. Delete the node whose degree is lower than k in the derived subgraph of community result C from community result C. If the deleted node is a member of the necessary node set, stop and return to the empty set, otherwise go to step b09.
The community search method according to claim 5, wherein the manner of searching while filtering includes:

c01, the necessary node set is classified into community result C;

c02. Obtain the derived subgraph corresponding to the community result C according to the given network graph. If the derived subgraph of the community result C has only one connected component and the degree of all nodes in the derived subgraph is greater than or equal to the given threshold k, then stop and return to the community result C;

c03, according to the connected components of the derived subgraph, the nodes in the same component are grouped into the same group;

c04, the neighbor nodes of all nodes in the community result C are classified into the candidate node set Candidate, and the nodes and banned nodes that already exist in the community result C are excluded from it;

c05, if Candidate is empty, set the community result C to an empty set, and go to step c08;

c06, for each node in the candidate node set Candidate, record the number a of the connected different connected components, the number b of the nodes in the connection community result C, the number of nodes connected to non-prohibited nodes in the given network graph d; Sort the nodes in the node set in multi-key descending order according to a, b, and d;

c07, if the degree of the top candidate node c is less than the threshold k, then remove the node c from the candidate node set Candidate, go to step c06, otherwise, add the node c to the community result C, and at the same time the neighbor node of the node Add to candidate node set Candidate, and remove node c from candidate node set Candidate, go to step c02;

c08, classify the nodes of the entire network graph into the community result C and delete the prohibited nodes;

c09, if the number of connected components of the derived subgraph of the community result C is greater than 1, stop and return to the empty set. If the degree of the smallest point in the derived subgraph of the community result C is greater than or equal to the threshold k, check the community result C at this time Whether it contains prohibited nodes;

c10. Delete the node whose degree is lower than k in the derived subgraph of the community result C from the community result C. If the deleted node is a member of the necessary node set, stop and return to the empty set, otherwise go to step c09.