Binary network module identification method and system based on entropy production, and storage medium

Abstract

The invention discloses a binary network module identification method and system based on entropy production, and a storage medium. The method comprises the following steps: a network conversion step:converting a binary network into a single-type node probability network only containing a type of nodes according to a probability relationship between two types of nodes in the binary network; a single-type node clustering step: calculating the information entropy of each relationship edge in the single-type node probability network, and performing clustering on the nodes in the single-type nodeprobability network according to the principle of information entropy production to obtain a single-type node initial cluster only containing a type of nodes; and a step of adding another type of nodes and a relationship edge: adding the other type of nodes and the relationship edge for expressing the connection relationship between the two types of nodes to the single-type node initial cluster according to the binary network relationship to obtain a final module. The binary network module identification method disclosed by the invention are simple and feasible, can be operated without usingadditional parameters, moreover, is relatively high in module identification rate and has an important reference value for conducting research on complex networks and biological information networks.

Description

technical field [0001] The invention relates to the technical field of identification of complex network modules, in particular to a bipartite network module identification method, system and storage medium based on entropy increase. Background technique [0002] Bipartite network is an important manifestation of complex network. Many networks in real life present a dichotomous structure, such as: member and activity relationship network, movie and actor relationship network, disease and gene relationship network, microRNA (miRNA) and messager RNA (mRNA) regulatory network, etc. The characteristics of this kind of network are: it is composed of two types of nodes, and the connection edge only exists between nodes of different types, and there is no connection edge between nodes of the same kind. Module is one of the most basic and important topological properties of complex networks, which can help us understand the structure and characteristics of the entire network. There...

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Problems solved by technology

However, choosing an appropriate module recognition algorithm to discover modules in complex networks can be difficult

Because many algorithms currently proposed, such as FN algorithm (Fast Newman) and TGA algorithm (Traditional Genetic Algorithm), are based on some cost functions, and the best results are obtained by tuning parameters on a specific network. If these algorithms are applied to other networks, The recognition accuracy is often low, and the recognition results are difficult to guarantee

The current algorithm needs to set parameters first, and there are problems such as complex algorithm, poor universality, and low recognition rate.

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