Label propagation overlapping community detection method based on core community attraction mechanism

Abstract

The application provides a label propagation overlapping community detection method based on a core community attraction mechanism, comprising the following steps: constructing a network model and preprocessing; identifying core communities in the network model and assigning a unique label to each core community; propagating the label and adjusting the propagation weight; allowing a node to belong to multiple core communities at the same time, adjusting the label assignment of the node according to the association degree of the node with different core communities, and forming an overlapping core community structure; evaluating the label propagation result; and adjusting the core community attraction mechanism and the label propagation parameter according to the evaluation result, and optimizing the core community detection effect. The application identifies the core communities in the network, and uses the core communities to guide the label propagation process, thereby enhancing the precision of the label propagation. The application adopts an adaptive mechanism, adjusts the label propagation path and weight in real time according to the interaction frequency and association degree between nodes, reduces unnecessary calculation overhead, and improves the propagation efficiency.

Description

Technical Field

[0001] The present invention relates to the field of data mining technology, and in particular to a label propagation overlapping community detection method based on a core community attraction mechanism. Background Art

[0002] With the continuous development of information-based society, complex networks are increasingly being used in various fields. Complex networks are not only widely used in social networks, biological networks, and communication networks, but also play a vital role in information dissemination, virus spread, and economic systems. In these complex networks, core community structure is one of their core characteristics. Core community detection, which identifies highly connected groups of nodes in a network, is an important means of understanding network structure and function. Traditional core community detection methods mostly focus on non-overlapping core communities. However, in many real-world networks, such as social and biological networks, nodes often belong to multiple core communities simultaneously. Therefore, overlapping core community detection has become a difficult problem that needs to be solved.

[0003] Among existing core community detection methods, label propagation algorithms have garnered widespread attention due to their simplicity and efficiency. Traditional label propagation algorithms detect core community structures by propagating and updating labels between nodes. However, this approach suffers from insufficient accuracy and efficiency when dealing with large-scale, complex networks. In particular, in overlapping core community detection, where nodes may belong to multiple core communities simultaneously, traditional label propagation algorithms struggle to accurately distinguish and detect these overlapping core communities, resulting in inaccurate results.

[0004] The application of traditional label propagation algorithms to large-scale, complex networks also faces several challenges. First, due to the complexity and diversity of networks, the label propagation process is susceptible to noise interference, affecting the accuracy of core community detection. Second, as the network scale increases, the computational workload increases dramatically, slowing the algorithm's convergence and affecting overall performance. Furthermore, traditional methods struggle to achieve efficient computation and rapid convergence while ensuring accuracy, which poses significant limitations in practical applications. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a label propagation overlapping community detection method based on the core community attraction mechanism. By introducing the core community attraction mechanism, the present invention can more accurately identify and distinguish overlapping core communities, reduce noise interference, and improve detection accuracy. At the same time, by optimizing the calculation process, the convergence speed of the algorithm is significantly improved, achieving efficient calculation.

[0006] The technical solution of the present invention is: a label propagation overlapping community detection method based on a core community attraction mechanism, comprising the following steps:

[0007] S1) Construct a network model by representing nodes as individuals and edges as relationships between individuals;

[0008] S2), preprocessing the network model G;

[0009] S3), identifying the core community in the network model G by a method based on node degree centrality;

[0010] S4), initialize the label propagation process and assign a unique label to each core community;

[0011] S5) Starting from the core community, the label is propagated according to the correlation between the node and the core community, and the propagation weight is adjusted based on the number and weight of the edges;

[0012] S6), gradually covering the entire network model G to ensure that all nodes are assigned labels;

[0013] S7) Allowing nodes to belong to multiple core communities at the same time, adjusting the node label assignment according to the degree of association between the node and different core communities, and forming an overlapping core community structure;

[0014] S8), evaluate the label propagation results, and evaluate the core community division results through modularity and core community density;

[0015] S9) According to the evaluation results, adjust the core community attraction mechanism and label propagation parameters to optimize the core community detection effect;

[0016] S10), repeat the label propagation and evaluation optimization process until the core community detection result reaches the preset standard.

[0017] Preferably, in step S1), the construction of the network model G specifically includes the following steps:

[0018] S11), define the node set V and edge set E of the network model; wherein, the node v∈V represents an individual, and the edge e∈E represents the relationship between individuals;

[0019] S12), by collecting network data, determining the initial set of nodes v and edges e, and recording the attribute information of each node and edge;

[0020] S13) Construct an adjacency matrix A, where the element A of the adjacency matrix A is ij Represents node v i and node v j If there is an edge e ij , then A ij =1, otherwise A ij =0;

[0021] S14), for each edge eij Assign weight w ij ;

[0022] S15) Construct the graph structure of the network model, namely:

[0023] G=(V,E,W);

[0024] Among them, V represents the node set, E represents the edge set, and W represents the edge weight set.

[0025] As a preference, in step S14), the weight w ij Determined by the strength or frequency of the relationship between nodes, including using the number of connections or interaction frequency to determine the weight w ij .

[0026] Preferably, in step S2), preprocessing the network model G includes removing noise nodes and edges, and normalizing the weights of the edges.

[0027] Preferably, in step S3), the core community in the network model G is identified based on the node degree centrality method, which specifically includes the following steps:

[0028] S31), calculating the degree d(v) of each node v∈V, where the degree d(v) represents the number of edges directly connected to the node v;

[0029] S32), based on the degree centrality of the nodes, determine the initial core node set C, where nodes with higher degree centrality are preferentially selected as core nodes;

[0030] S33), further screening core nodes by calculating the weighted connectivity between nodes to form the final core community set;

[0031] S34), introduce weighted modularity Q w As an evaluation indicator, determine the boundaries of the core community;

[0032] S35), according to the weighted modularity Q w Based on the calculation results, the scope of the core community is adjusted to ensure that the core community has strong internal connectivity and clear boundaries;

[0033] S36), using the identified core communities as the starting point of the label propagation process, and assigning a unique label to each core community.

[0034] Preferably, in step S4), each core community is assigned a unique label, which specifically includes the following steps:

[0035] S41) For each core community C i ∈C assigns a unique label L i, where i is the index of the core community;

[0036] S42) Set the initial labels of all nodes in each core community to the label L of the core community. i , that is, for the core community C i All nodes v∈C i , set L(v) = L i ;

[0037] S43), initializing a label set L(v) on each node in the network model G, wherein the label set L(v) includes the label of the core community to which the node belongs;

[0038] S44) Record each node and its corresponding label set L(v) to form an initial label distribution.

[0039] Preferably, in step S5), starting from the core community, labels are propagated according to the association between the nodes and the core community, and the propagation weight is adjusted based on the number and weight of the edges, specifically including the following steps:

[0040] S51) From each core community C i Initially, nodes in the core community are selected as the starting points for label propagation;

[0041] S52) Calculate the relationship between each node v and the core community C i The correlation R(v,C i );

[0042] S53), according to the node v and the core community C i The correlation R(v,C i ), determine the priority of label propagation, and nodes with higher priority will propagate labels first;

[0043] S54) During the label propagation process, the label propagation weight W is dynamically adjusted based on the number of edges and the weight of the edges. p (v);

[0044] S55), label L i Spread to the nodes directly connected to the core community nodes, and according to the propagation weight W p (v) Dynamically update the label set L(v) of these nodes;

[0045] S56), repeat the label propagation process until all nodes receive the label from the core community.

[0046] Preferably, in step S7), a node is allowed to belong to multiple core communities at the same time, and the node label assignment is adjusted according to the degree of association between the node and different core communities to form an overlapping core community structure, which specifically includes the following steps:

[0047] S71), during the label propagation process, record the label set L(v) of each node v, where the initial label set comes from the labels of the core community;

[0048] S72), according to the correlation R(v,C i ), computing node v belongs to each core community C i The probability P(v,C i );

[0049] S73), according to the probability P(v,C i ), adjust the label set L(v) of each node v to ensure that the node can belong to multiple core communities at the same time;

[0050] S74), in each iteration, dynamically update the label set L(v) of each node based on the interaction between the node and its neighbor nodes;

[0051] S75), repeat the label propagation and adjustment process until the label set L(v) of all nodes is stable and an accurate overlapping core community structure is formed;

[0052] S76) Finally, the label set L(v) of each node is output, and the overlapping core community structure formed by the label set is evaluated and verified.

[0053] Preferably, in step S8), the label propagation result is evaluated, and the core community division result is evaluated by modularity and core community density, which specifically includes the following steps:

[0054] S81), perform a preliminary evaluation of the label propagation results and record the final label set L(v) of each node v;

[0055] S82), calculating the modularity Q of the core community division;

[0056] S83) Calculate the core community density D (C i );

[0057] S84) Further evaluate the core community division results, calculate the average core community density Dˉ and the average modularity Qˉ; and conduct a comprehensive evaluation of the label propagation results based on the average core community density Dˉ and the average modularity Qˉ;

[0058] S85) According to the evaluation results, make necessary adjustments and optimizations to the label propagation process, and finally output the evaluated and optimized core community division results and generate a corresponding evaluation report.

[0059] Preferably, in step S9), according to the evaluation results, the core community attraction mechanism and label propagation parameters are adjusted to optimize the core community detection effect, which specifically includes the following steps:

[0060] S91) According to the core community division evaluation results, analyze the modularity Q and core community density D (C i ) changes;

[0061] S92) Adjust the core community attraction mechanism by recalculating the degree centrality and weighted connectivity W(v i ,v j ), optimize the selection and allocation of core communities and redefine the core community set C;

[0062] S93), reinitializing the label propagation process according to the new core community set C, and assigning a unique label to each core community;

[0063] S94), adjusting label propagation parameters, including propagation weight and propagation probability;

[0064] S95) Optimize the label propagation algorithm and adopt an adaptive mechanism to adjust the label propagation path and weight in real time according to the interaction frequency and correlation between nodes;

[0065] S96), based on the correlation between the node and the core community R(v,C i ) and propagation weight W p (v), dynamically adjust the label set L(v) of each node;

[0066] S97) Through multiple iterations, the label propagation process is continuously adjusted and optimized.

[0067] The beneficial effects of the present invention are:

[0068] 1. This invention enhances the accuracy of label propagation by identifying core communities in the network and using these core communities to guide the label propagation process;

[0069] 2. Based on node degree centrality and weighted connectivity, this method ensures that the selected core communities have strong internal connectivity and clear boundaries, thereby accurately reflecting the actual structure of the network. It also allows nodes to belong to multiple core communities at the same time, and adjusts label assignment according to the degree of association between nodes and different core communities to form an overlapping core community structure.

[0070] 3. This invention calculates the association between nodes and core communities and dynamically adjusts them based on propagation weights and propagation probabilities, making the label propagation process more accurate and ultimately obtaining more detailed core community division results.

[0071] 4. The present invention adopts an adaptive mechanism to adjust the label propagation path and weight in real time according to the interaction frequency and correlation between nodes, reducing unnecessary computing overhead and improving propagation efficiency;

[0072] 5. This invention dynamically adjusts the propagation parameters, enabling the algorithm to converge quickly when processing large-scale complex networks. During the label propagation process, the propagation weight and propagation probability are precisely adjusted based on the number and weight of edges, ensuring the effectiveness and accuracy of label propagation, thereby reducing the amount of computation and improving algorithm performance.

[0073] 6. This invention uses indicators such as modularity and core community density to comprehensively evaluate the label propagation results to ensure the accuracy and rationality of the core community division results;

[0074] 7. This invention uses modularity to evaluate the overall quality of core community delineation and core community density to assess the degree of compactness within the core community, thereby providing comprehensive quality control for core community detection results. Based on the evaluation results, the core community attraction mechanism and label propagation parameters are dynamically adjusted to further optimize the core community detection effect. Through multiple iterations and continuous optimization, the final core community delineation results are ensured to have high accuracy and effectiveness.

[0075] 8. The present invention significantly reduces the amount of computation by introducing a core community attraction mechanism and dynamically adjusting and optimizing the label propagation process, enabling the algorithm to run efficiently even in large-scale complex networks. The adaptive label propagation mechanism enables the algorithm to converge quickly, reduces the number of iterations, and improves overall performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 Schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0077] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings:

[0078] like Figure 1 As shown, this embodiment provides a label propagation overlapping community detection method based on a core community attraction mechanism, including the following steps:

[0079] S1) Constructing a network model G by representing nodes as individuals and edges as relationships between individuals. This specifically includes the following steps:

[0080] S11), define the node set V and edge set E of the network model; wherein, the node v∈V represents an individual, and the edge e∈E represents the relationship between individuals;

[0081] S12), by collecting network data, determining the initial set of nodes v and edges e, and recording the attribute information of each node and edge;

[0082] S13) Construct an adjacency matrix A, where the element A of the adjacency matrix A is ij Represents node v i and node v j If the relationship between node v i and node v j There is an edge e between ij , then A ij =1, otherwise A ij =0; i, j represent the index of the node respectively;

[0083] S14), for each edge e ij Assign the corresponding weight w ij , the weight w ij Determined by the strength or frequency of the relationship between nodes, including using the number of connections or interaction frequency to determine the weight w ij ;

[0084] S15) Construct the graph structure of the network model, namely:

[0085] G=(V,E,W);

[0086] Among them, V represents the node set, E represents the edge set, and W represents the edge weight set.

[0087] S2), preprocessing the network model G;

[0088] The preprocessing of this embodiment is to clean and standardize the network data by formulating a set of preset rules. These preset rules are determined based on the properties of nodes and edges and the relationships between them; including removing noise nodes and edges, and standardizing the weights of edges.

[0089] In addition, this embodiment can also monitor the network status in real time, identify available resources and interference conditions, clean up and standardize network data, and through the spectrum sensing process help identify which parts of the network have interference and which parts have more resources, thereby providing reliable data support for subsequent core community identification.

[0090] S3) Identify the core community in the network model G by using a method based on node degree centrality; specifically, the following steps are included:

[0091] S31), calculating the degree d(v) of each node v∈V, where the degree d(v) represents the number of edges directly connected to the node v;

[0092] S32), based on the degree centrality of the nodes, determine the initial core node set C, where nodes with higher degree centrality are preferentially selected as core nodes;

[0093] S33), further screening core nodes by calculating the weighted connectivity between nodes to form a final core community set; the weighted connectivity W (v i , v j ) is calculated by the following formula:

[0094]

[0095] Among them, A ij Represents node v i and node v j The relationship between ij For node v i and node v j The weight of the edge between i ) and d(v j ) are node v i and node v j degree;

[0096] S34), introduce weighted modularity Q w As an evaluation indicator, the boundaries of the core community are determined; the weighted modularity Q w The expression is:

[0097]

[0098] Where mw is the total weight of all edges in the network; A ij Represents node v i and node v j The relationship between i and w j Node v i and node v j The total weight of δ(c i , c j ) represents the indicator function, which is used to determine the node v i and node v j Whether they belong to the same core community; when node v i and node v j The value is 1 when they belong to the same core community, otherwise it is 0; c i 、c j They are node points v i and node v j community tags.

[0099] S35), according to the weighted modularity Q wBased on the calculation results, the scope of the core community is adjusted to ensure that the core community has strong internal connectivity and clear boundaries;

[0100] S36), using the identified core communities as the starting point of the label propagation process, and assigning a unique label to each core community.

[0101] S4) Initialize the label propagation process and assign a unique label to each core community; specifically, the following steps are included:

[0102] S41) For each core community C i ∈C assigns a unique label L i , where i is the index of the core community;

[0103] S42) Set the initial labels of all nodes in each core community to the label L of the core community. i , that is, for the core community C i All nodes v∈C i , set L(v) = L i ;

[0104] S43), initializing a label set L(v) on each node in the network model G, wherein the label set L(v) includes the label of the core community to which the node belongs;

[0105] S44) Record each node and its corresponding label set L(v) to form an initial label distribution.

[0106] S5) Starting from the core community, the label is propagated according to the association between the node and the core community, and the propagation weight is adjusted based on the number and weight of the edges. The specific steps include the following:

[0107] S51) From each core community C i Initially, nodes in the core community are selected as the starting points for label propagation;

[0108] S52) Calculate the relationship between each node v and the core community C i The correlation R(v, C i ); the correlation R(v, C i ) is determined based on the number of edges between the node and the core community and the weight of the edge. The correlation R(v, C i ) is calculated as:

[0109]

[0110] Among them, A uv is the relationship between node u and node v. If there is an edge e between node u and node v uv , then A uv=1, otherwise A uv =0;w uv is the weight of the edge between node u and node v, d(u) and d(v) are the degrees of node u and node v respectively;

[0111] S53), according to the node v and the core community C i The correlation R(v, C i ), determine the priority of label propagation, and nodes with higher priority will propagate labels first;

[0112] S54) During the label propagation process, the label propagation weight W is dynamically adjusted based on the number of edges and the weight of the edges. p (v); The propagation weight W of the label p The expression of (v) is:

[0113]

[0114] Where N(v) represents the set of neighbor nodes directly connected to node v; R(u, C i ) represents the relationship between node u and core community C i The correlation degree of A uv is the relationship between node u and node v; d(u) is the degree of node u; w uv is the weight of the edge between node u and node v;

[0115] S55), label L i is propagated to nodes directly connected to the nodes in the core community and is calculated based on the propagation weight W p (v) Dynamically update the label set L(v) of these nodes;

[0116]

[0117] Where N(v) represents the set of neighbor nodes directly connected to node v; A uv is the relationship between node u and node v, w uv is the weight of the edge between node u and node v;

[0118] S56), repeat the label propagation process until all nodes receive the label from the core community.

[0119] S6), gradually covering the entire network model G to ensure that all nodes are assigned labels;

[0120] S7) Allowing nodes to belong to multiple core communities at the same time, adjusting node label assignments based on the degree of association between the node and different core communities, and forming an overlapping core community structure; specifically, the steps include:

[0121] S71), during the label propagation process, record the label set L(v) of each node v, where the initial label set comes from the labels of the core community;

[0122] S72), according to the correlation R(v, C i ), computing node v belongs to each core community C i The probability P(v, C i ),Right now:

[0123]

[0124] Among them, ∑ j R(v,C j ) represents the total degree of association between node v and all core communities;

[0125] S73), according to the probability P(v, C i ), adjust the label set L(v) of each node v to ensure that the node can belong to multiple core communities at the same time;

[0126] L(v)=∪ i {L i ·P(v,C i )}

[0127] Where, ∪ i Represents the set L of all possible core community labels i And the corresponding probability P(v, C i ) performs a union operation;

[0128] S74), in each iteration, dynamically update the label set L(v) of each node based on the interaction between the node and its neighbor nodes;

[0129] S75), repeat the label propagation and adjustment process until the label set L(v) of all nodes is stable and an accurate overlapping core community structure is formed;

[0130] S76) Finally, the label set L(v) of each node is output, and the overlapping core community structure formed by the label set is evaluated and verified.

[0131] S8) Evaluate the label propagation results and evaluate the core community division results through modularity and core community density; specifically, the following steps are included:

[0132] S81), perform a preliminary evaluation of the label propagation results and record the final label set L(v) of each node v;

[0133] S82), calculating the modularity Q of the core community division; the calculation formula of the modularity Q is:

[0134]

[0135] Where m is the total number of edges in the network model; A ij Represents node v i and node v j The relationship between d(v i ) represents node v i degree; d(v j ) represents node v j degree; δ(c i , c j ) represents the indicator function, which is used to determine the node v i and node v j Whether they belong to the same core community; when node v i and node v j The value is 1 when they belong to the same core community, otherwise it is 0; C i 、c j They are node points v i and node v j community tags.

[0136] S83) Calculate the core community density D (C i ); the core community density D(C i ) is calculated as:

[0137]

[0138] In the formula, |E(C i )|For the core community C i The number of edges in |V(C i )|For the core community C i The number of nodes in .

[0139] S84) Further evaluate the core community division results and calculate the average core community density and average modularity and based on the average core community density and average modularity Conduct a comprehensive evaluation of label propagation results;

[0140] The average core community density and average modularity The calculation formulas are:

[0141]

[0142]

[0143] Where, C is the total number of core communities; D(C i ) is the core community C i The density of Q(C i ) is the core community C i The modularity.

[0144] S85) According to the evaluation results, make necessary adjustments and optimizations to the label propagation process, and finally output the evaluated and optimized core community division results and generate a corresponding evaluation report.

[0145] S9) Based on the evaluation results, adjust the core community attraction mechanism and label propagation parameters to optimize the core community detection effect; specifically, the following steps are included:

[0146] S91) According to the core community division evaluation results, analyze the modularity Q and core community density D (C i ) changes in ΔQ and ΔD (C i );Right now:

[0147]

[0148]

[0149] Where C is the total number of core communities; m is the total number of edges in the network model; A ij Represents node v i and node v j The relationship between d(v i ) represents node v i degree; d(v i ) represents node v i degree; d(v j ) represents node v j degree; |E(C i )|For the core community C i The number of edges in |V(C i )|For the core community C i The number of nodes in

[0150] S92) Adjust the core community attraction mechanism by recalculating the degree centrality and weighted connectivity W(v i , v j ), optimize the selection and allocation of core communities and redefine the core community set C;

[0151]

[0152] Where wij is the node v i and node v j The weight of the edge between ij Represents node vi and node v j The relationship between d(v i ) represents node v i degree; d(v i ) represents node v i degree; d(v j ) represents node v j degree; w uv is the weight of the edge between node u and node v;

[0153] S93), reinitializing the label propagation process according to the new core community set C, and assigning a unique label to each core community;

[0154] S94), adjusting label propagation parameters, including propagation weight and propagation probability;

[0155] S95) Optimize the label propagation algorithm and adopt an adaptive mechanism to adjust the label propagation path and weight in real time according to the interaction frequency and correlation between nodes;

[0156]

[0157] Where AdaptiveWeight(v) is the adaptive weight of node v;

[0158] S96), based on the correlation between the node and the core community R (v, C i ) and propagation weight W p (v), dynamically adjust the label set L(v) of each node;

[0159]

[0160] Where, ∪ i Indicates; L i For the core community C i Label; R(v,C i ) is the node v and the core community C i The correlation degree of j R(v,C j ) represents the total association between node v and all core communities; A uv is the relationship between node u and node v, w uv is the weight of the edge between node u and node v;

[0161] S97) Continuously adjust and optimize the label transmission process through multiple iterations; namely:

[0162]

[0163] Where T is the number of iterations and σ is a control parameter used to balance the changes in modularity and core community density.

[0164] S10), repeat the label propagation and evaluation optimization process until the core community detection result reaches the preset standard.

[0165] The above embodiments and descriptions are only for explaining the principles and best embodiments of the present invention. Without departing from the spirit and scope of the present invention, the present invention may be subject to various changes and improvements, which shall fall within the scope of the invention to be protected.

Claims

1. Label propagation overlapping community detection method based on core community attraction mechanism, characterized by: The steps include: S1), building a network model; S2), preprocessing the network model; S3), identifying core communities in the network model through a method based on node degree centrality; S4), initialize the label propagation process and assign a unique label to each core community; S5), starting from the core community, propagate labels according to the correlation between nodes and the core community and adjust the propagation weight; S6), gradually covering the entire network model to ensure that all nodes are assigned labels; S7) Allowing nodes to belong to multiple core communities at the same time, adjusting node label assignments based on the degree of association between the node and different core communities, and forming an overlapping core community structure; specifically, the steps include: S71), during the label propagation process, record the label set L(v) of each node v, where the initial label set comes from the labels of the core community; S72), according to the correlation R(v,C i ), computing node v belongs to each core community C i The probability P(v,C i ); S73), according to the probability P(v,C i ), adjust the label set L(v) of each node v to ensure that the node can belong to multiple core communities at the same time; S74), in each iteration, dynamically update the label set L(v) of each node based on the interaction between the node and its neighbor nodes; S75), repeat the label propagation and adjustment process until the label set L(v) of all nodes is stable and an accurate overlapping core community structure is formed; S76), finally outputting the label set L(v) of each node, and evaluating and verifying the overlapping core community structure formed by the label set; S8) Evaluate the label propagation results and evaluate the core community division results through modularity and core community density; specifically, the following steps are included: S81), perform a preliminary evaluation of the label propagation results and record the final label set L(v) of each node v; S82), calculating the modularity Q of the core community division; S83) Calculate the core community density D (C i ); S84) Further evaluate the core community division results and calculate the average core community density and average modularity and based on the average core community density and average modularity Conduct a comprehensive evaluation of label dissemination results; S85) According to the evaluation results, make necessary adjustments and optimizations to the label propagation process, and finally output the evaluated and optimized core community division results and generate a corresponding evaluation report; S9) Based on the evaluation results, adjust the core community attraction mechanism and label propagation parameters to optimize the core community detection effect; specifically, the following steps are included: S91) According to the core community division evaluation results, analyze the modularity Q and core community density D (C i ) changes; S92) Adjust the core community attraction mechanism by recalculating the degree centrality and weighted connectivity W(v i ,v j ), optimize the selection and allocation of core communities and redefine the core community set C; S93), reinitializing the label propagation process according to the new core community set C, and assigning a unique label to each core community; S94), adjusting label propagation parameters, including propagation weight and propagation probability; S95) Optimize the label propagation algorithm and adopt an adaptive mechanism to adjust the label propagation path and weight in real time according to the interaction frequency and correlation between nodes; S96), based on the correlation between the node and the core community R(v,C i ) and propagation weight W p (v), dynamically adjust the label set L(v) of each node; S97), continuously adjust and optimize the label propagation process through multiple iterations; S10), repeat the label propagation and evaluation optimization process until the core community detection result reaches the preset standard.

2. The label propagation overlapping community detection method based on the core community attraction mechanism according to claim 1 is characterized by: In step S1), the construction of the network model G specifically includes the following steps: S11), define the node set V and edge set E of the network model; wherein, the node v∈V represents an individual, and the edge e∈E represents the relationship between individuals; S12), by collecting network data, determining the initial set of nodes v and edges e, and recording the attribute information of each node and edge; S13) Construct an adjacency matrix A, where the element A of the adjacency matrix A is ij Represents node v i and node v j If there is an edge e ij , then A ij =1, otherwise A ij =0; S14), for each edge e ij Assign weight w ij ; S15) Construct the graph structure of the network model, namely: G=(V,E,W); Among them, V represents the node set, E represents the edge set, and W represents the edge weight set.

3. The label propagation overlapping community detection method based on the core community attraction mechanism according to claim 2 is characterized by: In step S3), the core community in the network model G is identified based on the node degree centrality method, which specifically includes the following steps: S31), calculating the degree d(v) of each node v∈V, where the degree d(v) represents the number of edges directly connected to the node v; S32), based on the degree centrality of the nodes, determine the initial core node set C, where nodes with higher degree centrality are preferentially selected as core nodes; S33), further screening core nodes by calculating the weighted connectivity between nodes to form the final core community set; S34), introduce weighted modularity Q w As an evaluation indicator, determine the boundaries of the core community; S35), according to the weighted modularity Q w Based on the calculation results, the scope of the core community is adjusted to ensure that the core community has strong internal connectivity and clear boundaries; S36), using the identified core communities as the starting point of the label propagation process, and assigning a unique label to each core community.

4. The label propagation overlapping community detection method based on the core community attraction mechanism according to claim 3 is characterized by: In step S4), each core community is assigned a unique label, which specifically includes the following steps: S41) For each core community C i ∈C assigns a unique label L i , where i is the index of the core community; S42) Set the initial labels of all nodes in each core community to the label L of the core community. i , that is, for the core community C i All nodes v∈C i , set L(v) = L i ; S43), initializing a label set L(v) on each node in the network model G, wherein the label set L(v) includes the label of the core community to which the node belongs; S44) Record each node and its corresponding label set L(v) to form an initial label distribution.

5. The label propagation overlapping community detection method based on the core community attraction mechanism according to claim 4 is characterized by: In step S5), starting from the core community, labels are propagated according to the association between nodes and the core community, and the propagation weight is adjusted based on the number and weight of edges. Specifically, the steps include: S51) From each core community C i Initially, nodes in the core community are selected as the starting points for label propagation; S52) Calculate the relationship between each node v and the core community C i The correlation R(v,C i ); S53), according to the node v and the core community C i The correlation R(v,C i ), determine the priority of label propagation, and nodes with higher priority will propagate labels first; S54) During the label propagation process, the label propagation weight W is dynamically adjusted based on the number of edges and the weight of the edges. p (v); S55), label L i Spread to the nodes directly connected to the core community nodes, and according to the propagation weight W p (v) Dynamically update the label set L(v) of these nodes; S56), repeat the label propagation process until all nodes receive the label from the core community.

6. The label propagation overlapping community detection method based on the core community attraction mechanism according to claim 3 is characterized by: In step S3), the weighted connectivity W(v i ,v j ) is calculated by the following formula: Among them, A ij Represents node v i and node v j The relationship between ij For node v i and node v j The weight of the edge between i ) and d(v j ) are nodes v i and node v j The degree of weighted modularity Q w The expression is: Among them, m w is the total weight of all edges in the network; A ij Represents node v i and node v j The relationship between i and w j Node v i and node v j The total weight of δ(c i ,c j ) represents the indicator function, which is used to judge the node v i and node v j Whether they belong to the same core community; when node v i and node v j The value is 1 when they belong to the same core community, otherwise it is 0; c i 、c j They are node points v i and node v j community tags.

7. The label propagation overlapping community detection method based on the core community attraction mechanism according to claim 5 is characterized by: In step S5), the correlation R(v,C i ) is calculated as: Among them, A uv is the relationship between node u and node v. If there is an edge e between node u and node v uv , then A uv =1, otherwise A uv =0;w uv is the weight of the edge between node u and node v, d(u) and d(v) are the degrees of node u and node v respectively; The label propagation weight W p The expression of (v) is: Where N(v) represents the set of neighbor nodes directly connected to node v; R(u,C i ) represents the relationship between node u and core community C i The correlation degree of A uv is the relationship between node u and node v; d(u) is the degree of node u; w uv is the weight of the edge between node u and node v.

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