Dynamic community detection method based on information dynamics

Abstract

The invention discloses a dynamic community detection method based on information dynamics, and the method comprises the following steps: S1, initial community recognition: S11, inputting an undirected network graph G = (V, E); S12, initializing information Iv of each node v in the network, and calculating a Jaccard similarity coefficient Suv and connection strength Huv between the nodes; S13, calculating neighbor node average similarity Avg _ S (v) and average degree Avg _ D (v) of the node v; S14, simulating an information dynamics interaction process between nodes by using an information dynamics model until a balance state is reached; S15, performing community division according to the information amount, and neighbor nodes with the same information amount are divided into the same community; S16, outputting an initial community C; S2, incremental community detection; S21, extracting a changed sub-graph delta Gi; S22, detecting a community delta Ci corresponding to the sub-graph delta Gi; S23, calculating a community C'i-1 which is not changed; S24, calculating a community Ci of the time slice Ti; and S25, repeating the steps S21 to S24 until Ti detection in all time slices iscompleted.

Description

technical field [0001] The invention relates to the field of information dissemination, in particular to a dynamic community detection method based on information dynamics. Background technique [0002] Community structure is the most prominent structural feature of complex networks. Vertices in the network can be naturally divided into multiple groups. The connections between vertices within the same group are relatively dense, while the edges between vertices in different groups are relatively sparse. Each group is a "community". [0003] In complex networks, communities often correspond to functional units of the network. For example, the grouping of Web pages with the same theme in the WWW network; functional modules in the protein molecular interaction network, metabolic channels in the metabolic network; a group of people with common characteristics in the social network, such as scientists with the same research direction in the cooperative relationship network A re...

AI Technical Summary

Problems solved by technology

[0005] The existing dynamic network community detection methods have a relatively complex structure, and when detecting the community structure of a certain time slice, they can only detect a single time slice sequentially, which has high computational complexity and cumbersome calculation steps, which seriously affects detection efficiency of dynamic communities

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