Complex network clustering method based on key influence of nodes

Abstract

The invention discloses a complex network clustering method based on the key influence of nodes, which comprises the following steps: sequencing nodes in a complex network according to the size of degree, wherein the subordinate community of each initial node is not determined; taking a node (the subordinate community thereof is not determined) with the largest degree as a core node of a community; beginning to build the community; determining the subordinate community of a node adjacent to the core node; after the community is completely built, taking a node (the subordinate community thereof is not determined) with the largest degree as a core node of a community; beginning to build a community; and repeatedly performing a process of community building until the subordinate communities of all nodes in the network are determined, thereby obtaining a final network cluster structure. By using the method disclosed by the invention, a clustering precision in the complex network is superior to that obtained by using a FN (functional network) clustering method; and the method plays a positive role in carrying out fine-grained reveal on a real cluster structure of the complex network.

Description

technical field [0001] The invention belongs to the field of data mining of community networks, relates to a clustering method, in particular to a complex network clustering method based on node influence. Background technique [0002] In the 21st century, mankind has entered the era of globalization, and the degree of global information networks has continued to deepen. With the discovery of small-world effects and scale-free properties in real-world networks, there has been an upsurge in research on complex networks. Complex network involves fields such as graph theory, statistical physics, computer network research, ecology, sociology and economics, and has strong interdisciplinary characteristics. The complex networks involved in the research mainly include: various networks in the field of life sciences (such as cell networks, protein networks), Internet / WWW networks, technical networks (such as power grids, electronic circuit networks), social networks (such as social ...

AI Technical Summary

Problems solved by technology

[0011] Aiming at the problem that the existing clustering method cannot accurately describe the real network cluster structure, and with the continuous expansion of the complex network scale, the clustering operation consumes more and more time and resources, it proposes a method based on node influence Force's complex network clustering method (Improved Fast-Newman Algorithm in Complex Networks Based on Core Influence)

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