Modeling Community Evolution Characteristics of Dynamic Networks with Evolutionary Bayesian Nonnegative Matrix Factorization

Wei Yu,Minghu Tang,Xiaoming Li,Huaming Wu,Yang Yu,Xue Chen,Wenjun Wang,M De Aguiar

doi:10.1155/2021/7215888

Abstract

In most cases, the block structures and evolution characteristics always coexist in dynamic networks. This leads to inaccurate results of temporal community structure analysis with a two-step strategy. Fortunately, a few approaches take the evolution characteristics into account for modeling temporal community structures. But the number of communities cannot be determined automatically. Therefore, a model, Evolutionary Bayesian Nonnegative Matrix Factorization (EvoBNMF), is proposed in this paper. It focuses on modeling the temporal community structures with evolution characteristics. More specifically, the evolution behavior, which is introduced into EvoBNMF, can quantify the transfer intensity of communities between adjacent snapshots for modeling the evolution characteristics. Innovatively, the most appropriate number of communities can be determined autonomously by shrinking the corresponding evolution behaviors. Experimental results show that our approach has superior performance on temporal community detection with the virtue of autonomous determination of the number of communities.

Highlights

Dynamic network analysis, as an important branch of complex network science, has attracted wide attention in recent years [1]
We introduce the evolution behaviors to model the evolution characteristics of community structures with Bayesian Nonnegative Matrix Factorization (BNMF) [8] in an evolutionary clustering framework [4]. en, we develop a gradient descent algorithm to optimize the parameters of Evolutionary Bayesian Nonnegative Matrix Factorization (EvoBNMF) by maximizing the posterior estimate
The evolution matrices correspond to quantitative results of the evolution behavior of communities. e community evolution matrix Z(t) mined from the dynamic network through EvoBNMF can represent its evolution pattern and reflect the community evolution relationship between adjacent snapshots

Summary

Introduction

As an important branch of complex network science, has attracted wide attention in recent years [1]. Ese works ignore the evolution characteristics of community structures when doing the temporal community detection. The block structures and evolution characteristics always coexist in dynamic networks. Erefore, it is very necessary to propose a model which describes the community structures with evolution characteristics for improving the accuracy of temporal community detection. Temporal community detection has been widely concerned, which focuses on mining the meaningful block structures or functional modules hiding in the network snapshots of dynamic networks. Ese types of approaches detect the communities of current snapshots ignoring the historical community structures from last snapshots, which take away Twostage approaches are introduced into temporal community detection, which first detect communities on each snapshot with a static method and match them across different snapshots [3]. ese types of approaches detect the communities of current snapshots ignoring the historical community structures from last snapshots, which take away

Methods

Results

Conclusion