Bridge node identification method based on hypergraph entropy

Abstract

The present invention belongs to the technical field of network analysis, and relates to a bridge node identification method based on the hypergraph entropy. A relatively stable triangle element is used to construct a super edge, and a super edge is used to represent the entire network. The algorithm is characterized in that: the hypergraph entropy, the Jaccard similarity coefficient, and other factors are considered in the algorithm; the hypergraph entropy provides a basis for measuring the quality of community division, and the hypergraph entropy takes into account the connection relationship inside the community and the connection relationship between the communities, wherein the smaller the hypergraph entropy is, the looser the connection relationship between the communities is, and the closer the connection relationship inside the community is, so that the community identification result is ideal; in the algorithm iteration process, when deleting or adding a "candidate super edge", whether to perform deletion or addition can be determined according to whether the hypergraph entropy can be reduced; the Jaccard similarity coefficient makes the identified similarity of communities not be too large, and when the similarity between two communities is greater than the average similarity, the two communities will be merged, so that community identification accuracy can be greatlyimproved; and finally, by identifying the intersection between the communities, the bridge nodes are obtained.

Description

technical field [0001] The invention belongs to the technical field of network analysis, and relates to a bridge node identification method based on hypergraph entropy, which is a bridge node identification method combining entropy, Jaccard similarity coefficient and hypernetwork. Background technique [0002] In recent years, research on the analysis and visualization of large-scale complex networks has become more and more common in many different fields, such as sociology, biology, and computer science. Often, these networks are relevant to the modeling of some complex systems. This new research field aims to characterize some important properties of complex networks. Although these networks come from different disciplines, they share common statistical and structural properties: small world, scale-free, and community structure. The community structure means that the vertices in the grid can be divided into several groups, and the vertex connections in each group are re...

AI Technical Summary

Problems solved by technology

The traditional team recognition cannot exist the problem of not being able to recognize overlap (community overlap)

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