scholarly journals Semisupervised Community Detection by Voltage Drops

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Min Ji ◽  
Dawei Zhang ◽  
Fuding Xie ◽  
Ying Zhang ◽  
Yong Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Potential Theory ◽  
Community Detection ◽  
Detection Algorithm ◽  
Label Propagation ◽  
Machine Learning Methods ◽  
Voltage Drops ◽  
Discrete Potential Theory ◽  
Community Detection Algorithm ◽  
The Relationship

Many applications show that semisupervised community detection is one of the important topics and has attracted considerable attention in the study of complex network. In this paper, based on notion of voltage drops and discrete potential theory, a simple and fast semisupervised community detection algorithm is proposed. The label propagation through discrete potential transmission is accomplished by using voltage drops. The complexity of the proposal isOV+Efor the sparse network withVvertices andEedges. The obtained voltage value of a vertex can be reflected clearly in the relationship between the vertex and community. The experimental results on four real networks and three benchmarks indicate that the proposed algorithm is effective and flexible. Furthermore, this algorithm is easily applied to graph-based machine learning methods.

scholarly journals A Neighborhood-Impact Based Community Detection Algorithm via Discrete PSO

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Dongqing Zhou ◽  
Xing Wang
Keyword(s):  
Community Detection ◽  
Real Life ◽  
Detection Algorithm ◽  
Label Propagation ◽  
Detection Problem ◽  
Signed Networks ◽  
Discrete Pso ◽  
Community Detection Algorithm ◽  
Definition Of ◽  
The Impact

The paper addresses particle swarm optimization (PSO) into community detection problem, and an algorithm based on new label strategy is proposed. In contrast with other label propagation strategies, the main contribution of this paper is to design the definition of the impact of node and take it into use. Special initialization and update approaches based on it are designed in order to make full use of it. Experiments on synthetic and real-life networks show the effectiveness of proposed strategy. Furthermore, this strategy is extended to signed networks, and the corresponding objective function which is called modularity density is modified to be used in signed networks. Experiments on real-life networks also demonstrate that it is an efficacious way to solve community detection problem.

Link community detection based on line graphs with a novel link similarity measure

2016 ◽  
Vol 30 (06) ◽  
pp. 1650023 ◽  
Author(s):  
Guishen Wang ◽  
Lan Huang ◽  
Yan Wang ◽  
Wei Pang ◽  
Qin Ma
Keyword(s):  
Community Detection ◽  
Similarity Measure ◽  
Detection Algorithm ◽  
Line Graphs ◽  
The Novel ◽  
On Line ◽  
Community Detection Algorithm ◽  
Relationship Of ◽  
The Relationship ◽  
Link Similarity

Link community gradually unfolds its capacity in complex network research. In this paper, a novel link similarity measure on line graphs is proposed. This measure can be adapted to different types of networks with an adjustable parameter. We prove its value converges to a limit on line graphs with the relationship of the nonneighbor links taken into account. Based on this similarity measure, we propose a novel link community detection algorithm for link clustering on line graphs. The detection algorithm combines the novel link similarity measure with the classic Markov Cluster (MCL) Algorithm and determines the link community partitions by calculating an extended modularity measure. Extensive experiments on two types of complex networks demonstrate the effectiveness, reliability and rationality of our solution in contrast to the other two classical algorithms.

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