Community division method and device for nodes and computer readable storage medium

Abstract

The invention discloses a community division method and device for nodes and a computer readable storage medium. The method comprises the steps of obtaining a network topology structure; calculating the importance between a node i and a neighbor node j in the network topology structure; calculating the difference between the node i and the neighbor node j based on the community division coefficient matrix of the node i and the neighbor node j by utilizing the generation model; determining an optimized link coefficient matrix of the network topology structure based on the importance and difference between the node i and the neighbor node j; and determining a community division result of the node i based on the optimized link coefficient matrix.

Description

technical field [0001] The present invention relates to data mining, in particular to a method, device and computer-readable storage medium for community division of nodes. Background technique [0002] In the prior art, methods for community division of nodes include: modularity, random walk, and centrality. The above methods all assign an objective function or use some heuristic algorithms to assign nodes to different communities, but The above discriminant algorithm does not take into account how the real network structure is generated, for example, as figure 1 As shown, according to the existing technology, the result of community division must be as follows figure 2 As shown, after all, nodes I, F, and N seem to be "closer" than I, G, and J. But in reality, I don't want to be like this. With I's personality, it is willing to give up F and N, because these two nodes do not have a huge topology, and I will spend more "energy" on G and J, Especially on the point of J, ...

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Problems solved by technology

[0002] In the prior art, methods for community division of nodes include: modularity, random walk, and centrality. The above methods all assign an objective function or use some heuristic algorithms to assign nodes to different communities, but The above discriminant algorithm does not take into account how the real network structure is generated, for example, as figure 1 As shown, according to the existing technology, the result of community division must be as follows figure 2 As shown, after all, nodes I, F, and N seem to be "closer" than I, G, and J

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