Multi-triangular group similarity cohesion based overlapping community discovery method applicable to TCMF (Traditional Chinese Medicine Formula) network

Abstract

The invention provides a multi-triangular group similarity cohesion based overlapping community discovery method applicable to a TCMF network. The method comprises the following steps of: 1) a stage of finding initial communities: a. finding a central point and initializing the communities; b. searching for multiple triangular groups and adding the triangular groups to the initial communities; c. repeating the steps until the degree of the central point is less than a threshold value; d. adding undistributed nodes to the initial communities; e. ending; 2) a stage of combining the initial communities: a. calculating the similarity between every two communities; b. selecting two most similar communities to be combined; and c. repeating the steps until the end. The multi-triangular group similarity cohesion based overlapping community discovery method is applicable to the TCMF network, and the accuracy of community division is greatly improved by using a multi-triangular technology, so that structure information of drug group communities can be accurately found from the TCMF network.

Description

technical field [0001] The invention relates to a method for excavating traditional Chinese medicine prescriptions, in particular to an overlapping community discovery method based on multi-triangular group similarity aggregation applicable to TCMF networks. Background technique [0002] Research on network community structure in complex network analysis has a long history, which involves various fields such as computer science, sociology, and life science. Analyzing the community structure in the network is very important for analyzing the network structure and understanding the characteristics of the network. There are many real complex networks in the real world, such as social networks, interlinking networks between Internet pages, literature citation networks, biological networks (such as protein interaction networks), paper co-authoring networks, and so on. Different real network structures represent the practical significance of their research directions. The Traditi...

AI Technical Summary

Problems solved by technology

Among them, the complexity of finding the initial community stage in this method is high, and the accuracy of the community discovery of traditional Chinese medicine groups in the TCMF network needs to be improved.

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