A method for estimating the number of complex network communities

Abstract

The invention relates to the field of complex network mining, in particular to a method for estimating the number of complex network communities. The method for estimating the number of community in complex network is disclosed. The complex network is denoted as graph and denoted as network G (V, E). The network G (V, E) contain m network nodes, denoted as V= (v1, v2,..., vm), wherein the ith network node (1 <= i <= m) is denoted as vi; There are n connections between nodes in the network, which are represented by n edges, denoted as E= (e1, e2, ..., en), where the first edge (1 <=l<= n) is denoted as el; the number of communities contained in the network G (V, E) and the community center nodes are determined. Without any prior information, it can fully reflect the intrinsic structural characteristics of dense network community and sparse network externality. Estimation accuracy of the number of communities in the network is high, which is conducive to improve the estimation performance of network community discovery algorithm, and has high practical value for the analysis of real network data.

Description

technical field

[0001] The invention relates to the field of complex network mining, in particular to a method for estimating the number of complex network communities. Background technique

[0002] A complex network generally refers to a network with a large number of nodes and complex connections. The rapid development of Internet technology and the explosive growth of information have brought human society into the Internet age. Our mass production and practice activities are in various complex network environments, such as social networks, protein networks, disease transmission networks, Internet networks, etc. . The uneven distribution of links in these complex networks implies the existence of community structure in the network, and a large number of studies have also shown that the network is usually composed of some communities with obvious structures. The connections between nodes within a community are relatively tight, while the connections between communities a...

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