Integrating attributes of nodes solves the community structure partition effectively

Abstract

Revealing ideal community structure efficiently is very important for scientists from many fields. However, it is difficult to infer an ideal community division structure by only analyzing the topology information due to the increment and complication of the social network. Recent research on community detection uncovers that its performance could be improved by incorporating the node attribute information. Along this direction, this paper improves the Blondel–Guillaume–Lambiotte (BGL) method, which is a fast algorithm based on modularity maximization, by integrating the community attribute entropy. To fulfill this goal, our algorithm minimizes the community attribute entropy by removing the boundary nodes which are generated in the modularity maximization at each iteration. By this way, the communities detected by our algorithm make a balance between modularity maximization and community attribute entropy minimization. In addition, another merit of our algorithm is that it is free of parameters. Comprehensive experiments have been conducted on both artificial and real networks to compare the proposed community detection algorithm with several state-of-the-art ones. As the experimental results indicate, our algorithm demonstrates superior performance.

Detecting Community Structures in Networks by Label Propagation with Prediction of Percolation Transition

“…In most cases, the groups of vertices in a network identified by a community detection algorithm are assumed to be communities irrespective of whether these groups satisfy a specific definition or not as mentioned in [ 6 , 8 ]. Then the quality of network division is measured by modularity, whose value is in [0, 1] [ 13 , 14 ].…”

“…Even in weighted networks, though they may consist of differentiated mass of connected vertices, there may still exist as distinct communities groups of vertices within which the edges are denser and between which the edges are sparser [ 3 ]. More and more algorithms are proposed and developed to detect the community structure, especially in recent years, such as Girvan-Newman algorithm (GN) [ 2 ], spectral clustering [ 4 ], spin-glass model [ 5 ], the algorithm proposed by Clauset, Newman, and Moore (CNM) [ 6 , 7 ], partition method using integrating attributes of vertices [ 8 ], and extremal optimization [ 9 ].…”

Multi-index algorithm of identifying important nodes in complex networks based on linear discriminant analysis

“…PCA is applied to confirm the weight of evaluating criteria, and GRA is used to calculate the importance of node. Therefore, being inspired by multi-index analysis algorithms and integrating the attributes of nodes 27,28 in this paper, the authors are proposing a new multi-index evaluation algorithm based on linear discriminant analysis (LDA) 29 for the node importance in complex network after synthesizing multi-index factors of node importance, including eigenvector centrality, betweenness centrality, closeness centrality, degree centrality, mutual-information, etc. In order to verify the validity of this algorithm, a series of simulation experiments have been done.…”