A novel complex networks clustering algorithm based on the core influence of nodes.

Chao Tong,Zhongyu Xie,Jianwei Niu,Bin Dai

doi:10.1155/2014/801854

Chao Tong, Zhongyu Xie + Show 2 more

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https://doi.org/10.1155/2014/801854

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Journal: TheScientificWorldJournal	Publication Date: Jan 1, 2014
Citations: 23	License type: CC BY 3.0

Affiliation: Beihang University, McGill University

Abstract

In complex networks, cluster structure, identified by the heterogeneity of nodes, has become a common and important topological property. Network clustering methods are thus significant for the study of complex networks. Currently, many typical clustering algorithms have some weakness like inaccuracy and slow convergence. In this paper, we propose a clustering algorithm by calculating the core influence of nodes. The clustering process is a simulation of the process of cluster formation in sociology. The algorithm detects the nodes with core influence through their betweenness centrality, and builds the cluster's core structure by discriminant functions. Next, the algorithm gets the final cluster structure after clustering the rest of the nodes in the network by optimizing method. Experiments on different datasets show that the clustering accuracy of this algorithm is superior to the classical clustering algorithm (Fast-Newman algorithm). It clusters faster and plays a positive role in revealing the real cluster structure of complex networks precisely.

Highlights

With the population of information networks and the discovery of the small world effect and the scale-free characteristic, research on complex networks has become a trend
To solve the above problems, we proposed a novel clustering algorithm based on the core influence of nodes
The dataset is a complex network of neurons in a living system, where each node represents a complete and independent neuron and the edge denotes the connection between neurons

Summary

Introduction

With the population of information networks and the discovery of the small world effect and the scale-free characteristic, research on complex networks has become a trend. Clustering algorithm plays a basic role in studying the cluster structure of complex networks. The MFC algorithm performs clustering based on connections and cannot be applied to the network with heterogeneous nodes. Based on the FN algorithm, Guimera and Amaral adopted the Q function as the optimization objective function and proposed a complex networks clustering algorithm based on simulated annealing (SA), the GA algorithm [11]. Optimization algorithms based on the Q function perform well in the community clustering, a number of issues remain unresolved due to the unpredictability of complex networks and the biased characteristic of Q function. (3) Though the clustering algorithm based on heuristics method is able to handle the large-scale data in complex networks, compared to the optimization algorithm, it has lower clustering accuracy and cannot give high-precision clustering results.

Clustering Algorithm Based on the Core Influence of Nodes

Experimental Results and Analysis

Conclusion