An Enhanced Community Detection Method Based on Neighborhood Similarity

2012 ◽  
Author(s):  
Shaoqian Zhang ◽  
Zhenxing Liu ◽  
Wanchun Dou
Keyword(s):  
Community Detection ◽  
Detection Method

Community detection in social networks based on information propagation and user engagement

2021 ◽  
pp. 2150119
Author(s):  
Fuzhong Nian ◽  
Li Luo ◽  
Xuelong Yu ◽  
Xin Guo
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Detection Method ◽  
User Engagement ◽  
Connection Strength ◽  
Information Propagation ◽  
Edge Strength ◽  
Strength Based ◽  
Network Propagation ◽  
Overlapping Community

The iterative propagation of information between nodes will strengthen the connection strength between nodes, and the network can evolve into different groups according to difference edge strength. Based on this observation, we present the user engagement to quantify the influences of users different propagation modes to network propagation, and construct weight network to simulate real social network, and proposed the community detection method in social networks based on information propagation and user engagement. Our method can produce different scale communities and overlapping community. We also applied our method to real-world social networks. The experiment proved that the network spread and the community division interact with each other. The community structure is significantly different in the network propagation of different scales.

A Community Detection Method Based on Multi-Objective Optimization Method

2014 ◽  
Vol 571-572 ◽  
pp. 177-182 ◽  
Author(s):  
Lu Wang ◽  
Yong Quan Liang ◽  
Qi Jia Tian ◽  
Jie Yang ◽  
Chao Song ◽  
...  
Keyword(s):  
Community Detection ◽  
Detection Method ◽  
Optimization Method ◽  
Research Area ◽  
Multi Objective ◽  
Detection Algorithms ◽  
Multi Objective Genetic Algorithm ◽  
Community Fitness ◽  
Active Research ◽  
Active Research Area

Community detection in complex network has been an active research area in data mining and machine learning. This paper proposed a community detection method based on multi-objective evolutionary algorithm, named CDMOEA, which tries to find the Pareto front by maximize two objectives, community score and community fitness. Fast and Elitist Multi-objective Genetic Algorithm is used to attained a set of optimal solutions, and then use Modularity function to choose the best one from them. The locus based adjacency representation is used to realize genetic representation, which ensures the effective connections of the nodes in the network during the process of population Initialization and other genetic operator. Uniform crossover is introduced to ensure population’s diversity. We compared it with some popular community detection algorithms in computer generated network and real world networks. Experiment results show that it is more efficient in community detection.

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