Social network key figure hiding method and device based on target k node decomposition

Abstract

The invention relates to a social network key figure hiding method and device based on target k node decomposition, and the method comprises the following steps: S1, assuming an undirected and unweighted network G, distributing a core value C (v, G) to each node in the network through employing a k core decomposition algorithm, and defining the node with the highest core value in the social network as a key figure Vk; s2, for each node, obtaining hierarchical weight information M of the node, and constructing a backtracking tree GBT for a target k node to obtain a candidate edge set H; and S3, according to the candidate edge set H, selecting the edge e with the most influenced nodes for trimming and deleting. And S4, repeating the step S3 until all nodes in the target k node are removed from the k nuclear subgraph, namely, achieving the key figure hiding effect. According to the method, an onion decomposition algorithm and a backtracking tree algorithm are improved, and a backtracking tree is constructed to obtain a set of least connected edges in a graph needing to be deleted, so that a specified target node is separated from a k-kernel subgraph, and the hiding of key figure information in a social network is realized.

Description

Technical Field

[0001] The present invention relates to the fields of data mining and social networks, and in particular to a method and device for hiding key figures in a social network based on target k-node decomposition. Background Art

[0002] With the development of Internet technology, the network has become an indispensable part of our daily life. It plays an important role in various complex systems, such as social networks, power networks, and transportation networks. Key nodes refer to nodes with important influence and connectivity in various networks. These nodes play a core role in the network, so the identification of key nodes plays an important role in the overall function and stability of the network. For example, social networks are important communication and information exchange platforms in modern society. With the continuous growth of the number and scale of social network users, the connection relationship between nodes in the network is becoming increasingly complex. In social networks, key nodes are usually individuals or groups with extensive social relationships and high influence. They can accelerate the spread of information, guide public opinion, and influence the behavior decisions of other users through their demonstration. Studies have shown that attacks on key nodes may have serious consequences. How to analyze and understand the important nodes and their relationships in these complex network structures has become an important research topic.

[0003] Due to its simplicity and efficiency, the k-core has gained wide attention as a key indicator for capturing the participation of network structures. The k-core is a maximally induced subgraph in which the degree of each node is at least k. The existence of nodes in the k-core depends on their neighbor relationships in the subgraph. Therefore, removing a part of the nodes or edges from the k-core may cause the separation of other nodes in the k-core. For example, some researchers have found that removing a small number of edges may seriously damage the structure of the k-core. Some scholars focus on the k-core minimization problem, using the cooperative game theory concept Shapley value and proposing effective algorithms to deal with this problem. Although the above methods are effective, they all attack the k-core from a global perspective and rarely pay attention to the perturbation of the distribution of individual nodes in the k-core. For example, the removal of key nodes in the k-core may significantly destroy the network structure. However, the perturbation of removing nodes is not feasible in practice and may lead to serious consequences. Therefore, for the sake of concealment and feasibility, by trying to identify critical edges, the removal of edges will trigger the separation of a given target node from the k-core, making the key nodes unidentifiable and achieving a hidden effect.

[0004] Chinese patent CN 114301674 A proposes a method for hiding key figures in social networks based on degree-preserving attack, which mainly achieves the purpose of protecting the privacy of social users by reconnecting some links of the original network without changing the degree value of the node, so that the node core value in the entire network changes. However, this method attacks the k-core from a global perspective and does not pay attention to the vulnerability of local nodes in the k-core. Summary of the invention

[0005] The present invention aims to overcome the above-mentioned shortcomings of the prior art and provides a method and device for hiding key figures in a social network based on target k-node decomposition.

[0006] The objective of the present invention is to selectively screen candidate edges for a target node for a given network and a target k-node set, so that all target nodes do not exist in the original k-core by removing the minimum set of edges. This problem is defined as the target k-node collapse problem. The method according to the present invention can collapse the target k-node by integrating the target node information into the candidate edge screening process, selectively screening candidate edges for the target node, and obtaining the minimum budget for removing edges, thereby reducing the computational complexity. In social networks, the study of the target k-node collapse problem helps to understand the node vulnerabilities in the k-core model, allowing maintenance personnel to protect key relationships between users. In addition, it also helps to reduce the importance of confidential users, thereby reducing the attention of potential attackers to them.

[0007] The present invention improves the onion decomposition algorithm and the backtracking tree algorithm, constructs a backtracking tree to obtain the minimum edge set in the graph that needs to be deleted, so that the specified target node is separated from the k-core, thereby realizing the concealment of the key person information in the network.

[0008] The first aspect of the present invention relates to a method for hiding key figures in a social network based on target k-node decomposition, comprising the following steps:

[0009] S1: Assume an undirected and unweighted network G and use the k-core decomposition algorithm to assign a core value C to each node in the network. (v,G) , define the node with the highest core value in the social network as the key person V k ;

[0010] S2: For each node, obtain its level weight information M and build a backtracking tree G for the target k node BT Get the candidate edge set H;

[0011] S3: Select the edge e with the most affected nodes according to the candidate edge set H for pruning and deletion;

[0012] S4: Repeat step S3 until all nodes in the target k node are removed from the k core, that is, the key person hiding effect is achieved.

[0013] Specifically, in step S1: assuming an undirected unweighted social network G(V,E), V is a set of nodes in the network, E is a set of links in the network, and a kernel value C is assigned to each node in the network using the k-core decomposition algorithm. (v,G) The core value of a node is equivalent to the importance of the node. The higher the core value of a node, the higher the importance of the node. The node with the highest core value in a social network is defined as the key person V. k .

[0014] The step S2 specifically includes the following steps:

[0015] S2.1: For each node in the network, find its neighbor nodes N (v,G) and node degree d (v,G) ;

[0016] S2.2: According to theorem, if and only if When removing the edge (u,v) will cause the k-node to collapse and theorem assumes k = C (u,G) ≤C (v,G) , after removing (u,v), only the node with k cores will crash, and its core number will be reduced by 1 at most, and the initial candidate edge set P = {(u,v)|(u,v)∈E,min(C (u,G) ,C (v,G) )=k};

[0017] S2.3: Use the improved onion decomposition algorithm to divide the k nodes of graph G into different layers, so as to further simplify the candidate edge set; first calculate the node degree value d of all nodes (v,G) , and sort the node degree values ​​to get the degree order, peel off the nodes from small to large according to the degree order, and divide the nodes into different layers according to how many peelings are needed to reach a given node; that is, if the degree of the node of the k-shell in the k-core is exactly k, it belongs to the first local layer; if after removing the first layer, the node with the node degree of at most k belongs to the second local layer, and so on to get the number of layers M for each node;

[0018] S2.4: According to the number of layers M, give priority to neighbors with higher collapse order, and use the breadth-first search algorithm to iteratively backtrack neighbors that increase the collapse order relative to the current node; first use the improved onion decomposition algorithm to calculate the layer value of each node, and initialize the queue to the target node set; then initialize an empty directed graph G BT , which contains a node set T and an empty edge set; then traverse each node u in the queue, find all nodes v adjacent to u and with a core number of k, and store these nodes in the set N′, then find all nodes v in the set with a layer number less than node u, and store these nodes in the set N * , update the queue, remove u from the queue, and replace all* In but not in the backtracking tree G BT The nodes in G are added to the queue and updated BT , all in N * Add the node v and edge (u,v) in G BT Finally, we get the simplified candidate edge set H = E BT ∪{(u,v)|u∈T,v∈N(u,G k )\V BT )};

[0019] Specifically, step S3 specifically includes: traversing each edge e in the candidate edge set H, and pruning edge e according to the pruning rule; first, removing edge e from the backtracking tree to obtain a new subgraph; at the same time, initializing the pruned follower set as the intersection of the follower set of edge e and the target node set T; then finding all the node sets Z in the target node set T that exclude zero-indegree nodes, removing node set Z from it, adding node set Z to the pruned follower set, and obtaining the pruned follower set; selecting edge e with the most followers after pruning. * , e * Add to the set of edges that were removed, and finally get the set of edges that were removed.

[0020] A second aspect of the present invention relates to a device for hiding key figures in a social network based on target k-node decomposition, comprising a memory and one or more processors, wherein the memory stores executable code, and when the one or more processors execute the executable code, they are used to implement a method for hiding key figures in a social network based on target k-node decomposition of the present invention.

[0021] A third aspect of the present invention relates to a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the method for hiding key figures in a social network based on target k-node decomposition of the present invention.

[0022] The innovation of this invention is that it proposes a method for hiding key figures in social networks based on target k-node decomposition. This method can separate the specified target node from the k-core by deleting the minimum set edge in the graph, thereby effectively avoiding the risk of user privacy leakage. The main innovations are as follows:

[0023] (1) An improved onion decomposition algorithm is proposed, and the judgment condition of node stripping is modified, so that it can perform a more detailed hierarchical division of k-core nodes. The present invention proposes an improved onion decomposition algorithm and applies it to the field of anonymization of key figures in social networks. Compared with the onion decomposition algorithm, the improved onion decomposition algorithm modifies the judgment condition of node stripping, so that it can perform a more detailed hierarchical division of k-core nodes, until a hierarchical weight information M is returned for all k nodes, thereby providing a basis for subsequent edge set screening and reduction. This method performs a more detailed hierarchical division of k-core nodes and can more effectively deal with the k-node stratification problem in social networks.

[0024] (2) A backtracking tree algorithm is proposed to screen candidate links of the target node, greatly reducing the number of candidate links. The present invention screens candidate links of the target node based on the node level weights obtained by the improved onion decomposition algorithm, and a backtracking tree algorithm is proposed for this purpose. The algorithm takes the target node as the root node, uses breadth-first search to traverse all neighbor nodes whose level weights are less than the current root node, and adds the traversed nodes to the backtracking tree G in order. bt In, G bt All the edges contained in are the candidate edges considered for crashing the target node. This method greatly reduces the number of candidate edges and greatly reduces the computational complexity.

[0025] (3) A target k-node attack algorithm based on the improved onion algorithm is proposed to obtain a reduced set of candidate links, and the link with the greatest influence among the candidate links is iteratively deleted until all target nodes collapse. After reducing the candidate links, the present invention designs a k-node attack algorithm based on the improved onion algorithm to achieve the collapse of the target k nodes. The algorithm iteratively deletes the link with the greatest influence among the candidate links until all target nodes collapse, thereby achieving the anonymization of key figures in the social network. This strategy greatly reduces the computational complexity by simplifying the candidate links, ensuring the efficiency of the anonymization process.

[0026] Therefore, the present invention proposes a method for hiding key figures in social networks based on target k-node decomposition, which has significant innovation and practicality in protecting user privacy, maintaining social network functions and improving security.

[0027] The present invention improves the onion decomposition algorithm and the backtracking tree algorithm, constructs a backtracking tree to obtain the minimum edge set in the graph that needs to be deleted, so that the specified target node is separated from the k-core subgraph, thereby realizing the concealment of key person information in the social network.

[0028] The advantages and effects of the present invention compared with the prior art are: the proposed method greatly reduces the candidate edge sets that need to be deleted in order to achieve the concealment effect, greatly reduces the calculation time, and improves the efficiency. By deleting the edges to change the k-core value of the key node, it can ensure that the nodes where the key figures in the social network are located are effectively concealed without being discovered by hacker attacks, thereby improving the security of the network. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0030] Figure 1 is a flow chart of the method of the present invention.

[0031] Figure 2 It is a flow chart of improving the onion decomposition algorithm in step S2 of the present invention.

[0032] Figure 3 The present invention is a schematic diagram generated after selecting some nodes in the network to perform an improved onion decomposition algorithm and construct a backtracking tree. Specific implementation methods

[0034] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as limiting the present invention, but should be understood as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0035] It should be understood that the terms described in the present invention are only for describing special embodiments and are not intended to limit the present invention. In addition, for the numerical range in the present invention, it should be understood that each intermediate value between the upper and lower limits of the scope is also specifically disclosed. Each smaller range between the intermediate value in any stated value or stated range and any other stated value or intermediate value in the described range is also included in the present invention. The upper and lower limits of these smaller ranges can be independently included or excluded in the scope.

[0036] Unless otherwise indicated, all technical and scientific terms used herein have the same meanings as those generally understood by those skilled in the art. Although the present invention describes only preferred methods and materials, any methods and materials similar or equivalent to those described herein may also be used in the implementation or testing of the present invention. All documents mentioned in the specification are incorporated by reference to disclose and describe the methods and / or materials related to the documents. In the event of a conflict with any incorporated document, the content of this specification shall prevail.

[0037] It will be apparent to those skilled in the art that various modifications and variations may be made to the specific embodiments of the present invention description without departing from the scope or spirit of the present invention. Other embodiments derived from the present invention description will be apparent to those skilled in the art. The present application description and examples are exemplary only.

[0038] The words “include,” “including,” “have,” “contain,” etc. used in this document are open-ended terms, meaning including but not limited to.

[0039] Unless otherwise specified, the "parts" described in the present invention are all based on mass parts.

[0040] Example 1

[0041] This embodiment provides a method for hiding key figures in social networks based on target k-node decomposition. With the widespread use of social networks, user privacy protection has received more and more attention. A social networking platform has millions of registered users, including many well-known public figures, corporate executives, and scientific researchers. The social dynamics and personal information of these key figures have received widespread attention, and the risk of privacy leakage is increasing. Using the users in the platform as nodes and the user relationships as edges, the hiding method proposed in the present invention can protect these key figures and avoid the impact of deliberate attacks on key figures. As shown in the figure, the implementation steps are specifically included:

[0042] Step S1: Define the social network as an undirected and unweighted network G(V,E), where V is the set of users in the network and E is the set of user relationships in the network; the small social network consists of seven users, namely v1~v7; first use the k-core decomposition algorithm to assign a core value C to each user in the network (v,G) , the user's core values ​​are: Define the user with the highest core value in the social network as the key person V k At this time, user v7 is a key person who needs to be hidden.

[0043] Step S2: For each user, obtain its hierarchical weight information M and construct a backtracking tree G for the target user BT Obtaining the candidate relationship edge set H includes the following steps:

[0044] S2.1: For each user in the network, find its neighbor users N (v,G) and node degree d (v,G) , the node degree values ​​are:

[0045] S2.2: According to theorem, if and only if When removing the edge (u,v) will cause the k-node to collapse and theorem assumes k = C (u,G) ≤C (v,G) , after removing (u, v), only the node with k cores will crash, and its core number will be reduced by 1 at most to obtain the initial candidate relationship edge set P = {(u, v)|(u, v)∈E,min(C (u,G) ,C (v,G) )=k}, for this network, the initial candidate relationship edge set P={(v1,v2),(v2,v3),(v3,v4),(v4,v5),(v5,v6),(v2,v7),(v5,v7)}.

[0046] S2.3: Use the improved onion decomposition algorithm to divide the users of network G into different layers, so as to further simplify the candidate relationship edge set; first calculate the node degree value d of all users (v,G) , and sort the node degree values ​​to get the degree order, peel off the user nodes from small to large according to the degree order, and divide the user nodes into different layers according to how many peelings are needed to reach a given user node; that is, if the degree of the node of the k-shell in the k-core is exactly k, it belongs to the first local layer; if after removing the first layer, the node with a node degree of at most k belongs to the second local layer, and so on to get the number of layers M where each user node is located, the number of layers are: M1=1, M3=1, M4=1, M6=1, M2=2, M5=2, M7=3.

[0047] S2.4: According to the number of layers M, give priority to neighbors with higher collapse orders, take the target user node as the root node, and perform breadth-first search to visit all neighbor nodes with a smaller number of layers than the current node; first use the improved onion decomposition algorithm to calculate the layer value of each node, and initialize the queue to the target node set; then initialize an empty directed graph G BT , which contains a node set T and an empty edge set; then traverse each node u in the queue, find all nodes v adjacent to u and with a core number of k, and store these nodes in the set N′, then find all nodes v in the set with a layer number less than node u, and store these nodes in the set N * , update the queue, remove u from the queue, and replace all * In but not in the backtracking tree G BT The nodes in G are added to the queue and updated BT , all in N * Add the node v and edge (u,v) in G BT Finally, we get the simplified candidate relationship edge set H = E BT ∪{(u,v)|u∈T,v∈N(u,G k )\V BT)}, for this network, the simplified candidate relationship edge set H = {(v1,v2),(v2,v3),(v2,v7),(v4,v5),(v5,v6),(v5,v7)}.

[0048] Step S3: Traverse each edge e in the candidate relationship edge set H and prune edge e according to the pruning rule. First, remove edge e from the backtracking tree to obtain a new subgraph network; at the same time, initialize the pruned follower set as the intersection of the follower set of edge e and the target node set T; then find all the node sets Z in the target node set T that exclude zero-in-degree nodes, remove node set Z from it, add node set Z to the pruned follower set, and obtain the pruned follower set; select the edge e with the most followers after pruning. * , e* 加 into the set of removed edges, and finally obtain the set of removed edges: {(v2,v7),(v5,v7)}.

[0049] Step S4: Repeat step S3 until all nodes in the target node are removed from the k-core; for this small social network, the first iteration deletes the edge between nodes v2 and v7 to obtain an updated subgraph; the second iteration deletes the edge between nodes v5 and v7, and the core position of node v7 is hidden. That is, the algorithm achieves the effect of hiding key figures in the social network.

[0050] By implementing the method for hiding key figures in social networks based on target k-node decomposition proposed by the present invention, the social media platform successfully protects the privacy of key figures and reduces the risk of user privacy leakage. At the same time, the method also provides a privacy protection solution that can be used as a reference for other similar platforms.

[0051] In summary, the present invention utilizes a method for hiding key figures in social networks based on target k-node decomposition, selectively screens candidate links for target nodes, overcomes the problem of low computing speed of previous key figure information hiding algorithms on large-scale networks, can successfully hide key figure nodes in social networks, effectively maintain the security of the network, and meet the requirements of practical use.

[0052] Example 2

[0053] The present embodiment relates to a device for hiding key figures in a social network based on target k-node decomposition, including a memory and one or more processors, wherein the memory stores executable code, and when the one or more processors execute the executable code, they are used to implement a method for hiding key figures in a social network based on target k-node decomposition according to the present embodiment 1.

[0054] Example 3

[0055] This embodiment relates to a computer-readable storage medium on which a program is stored. When the program is executed by a processor, the method for hiding key figures in a social network based on target k-node decomposition of this embodiment 1 is implemented.

[0056] The embodiments described above are only descriptions of the preferred modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for hiding key figures in social networks based on target k-node decomposition, characterized in that: The steps include: S1: Assign core values ​​to nodes in the social network and define key figures; S2: Obtain node level weight information, and build a backtracking tree for the target node to obtain the candidate edge set; S3: Select the most affected edge pruning and deletion based on the candidate edge set; S4: Repeat step S3 until all nodes in the target node are removed from the k-core subgraph, achieving the effect of hiding key figures.

2. The method for hiding key figures in a social network based on target k-node decomposition according to claim 1, characterized in that: The step S1 of assigning core values ​​to nodes in the social network and defining key figures specifically includes the following steps: S1.1: First, use the k-core decomposition algorithm to assign a core value C to each node in the network. (v,G) ,The core value of a node is equivalent to the importance of the node. The higher the core value of the node, the higher the importance of the node; S1.2: Define the node with the highest core value in the social network as the key person V k .

3. The method for hiding key figures in a social network based on target k-node decomposition according to claim 1, characterized in that: The step S2 of obtaining node level weight information and constructing a backtracking tree for the target node to obtain a candidate edge set specifically includes the following steps: S2.1: For each node in the network, find its neighbor nodes N (v,G) and node degree d (v,G) ; S2.2: According to theorem, if and only if When removing the edge (u,v) will cause the k-node to collapse and theorem assumes k = C (u,G) ≤C (v,G) , after removing (u,v), only the node with k cores will crash, and its core number will be reduced by 1 at most, and the initial candidate edge set P = {(u,v)|(u,v)∈E,min(C (u,G) ,C (v,G) )=k}; S2.3: Use the improved onion decomposition algorithm to divide the k nodes of graph G into different layers, so as to further simplify the candidate edge set; first calculate the node degree value d of all nodes (v,G) , and sort the node degree values ​​to get the degree order, peel off the nodes from small to large according to the degree order, and divide the nodes into different layers according to how many peelings are needed to reach a given node; that is, if the degree of the node of the k-shell in the k-core is exactly k, it belongs to the first local layer; if after removing the first layer, the node with the node degree of at most k belongs to the second local layer, and so on to get the number of layers M for each node; S2.4: According to the number of layers M, give priority to neighbors with higher collapse order, and use the breadth-first search algorithm to iteratively backtrack neighbors that increase the collapse order relative to the current node; first use the improved onion decomposition algorithm to calculate the layer value of each node, and initialize the queue to the target node set; then initialize an empty directed graph G BT , which contains a node set T and an empty edge set; then traverse each node u in the queue, find all nodes v adjacent to u and with a core number of k, and store these nodes in the set N′, then find all nodes v in the set with a layer number less than node u, and store these nodes in the set N * , update the queue, remove u from the queue, and replace all * In but not in the backtracking tree G BT The nodes in G are added to the queue and updated BT , all in N * Add the node v and edge (u,v) in G BT Finally, we get the simplified candidate edge set H = E BT ∪{(u,v)|u∈T,v∈N(u,G k )\V BT )}.

4. The method for hiding key figures in a social network based on target k-node decomposition according to claim 1, characterized in that: The step S3 of selecting the most affected edge to be pruned and deleted according to the candidate edge set specifically includes the following steps: S3.1: Traverse each edge e in the candidate edge set H and prune edge e according to the pruning rule; S3.2: Remove edge e from the backtracking tree to obtain a new subgraph, and initialize the pruned follower set to the intersection of the follower set of edge e and the target node set T; S3.3: Find the node set Z excluding the zero-indegree nodes in all target node sets T, remove the node set Z from the target node set T, add the node set Z to the pruned follower set, and obtain the pruned follower set; S3.4: Select the edge e with the most followers after pruning * , e * Add to the set of edges that were removed, and finally get the set of edges that were removed.

5. A device for hiding key figures in social networks based on target k-node decomposition, characterized in that: It comprises a memory and one or more processors, wherein the memory stores executable codes, and when the one or more processors execute the executable codes, they are used to implement a method for hiding key figures in a social network based on target k-node decomposition as described in any one of claims 1-4.

6. A computer-readable storage medium, characterized in that: A program is stored thereon, and when the program is executed by a processor, the method for hiding key figures in a social network based on target k-node decomposition according to any one of claims 1 to 4 is implemented.

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