A comparative analysis of evolutionary and memetic algorithms for community detection from signed social networks

2013 ◽  
Vol 18 (2) ◽  
pp. 329-348 ◽  
Author(s):  
Yadong Li ◽  
Jing Liu ◽  
Chenlong Liu
Keyword(s):  
Social Networks ◽  
Comparative Analysis ◽  
Community Detection ◽  
Memetic Algorithms ◽  
Signed Social Networks

scholarly journals COMPARATIVE ANALYSIS OF QUALITY METRICS FOR COMMUNITY DETECTION IN SOCIAL NETWORKS USING GENETIC ALGORITHM

2016 ◽  
Vol 26 (6) ◽  
pp. 625-641 ◽  
Author(s):  
Simrat Kaur ◽  
Sarbjeet Singh ◽  
Sakshi Kaushal ◽  
Arun Kumar Sangaiah
Keyword(s):  
Genetic Algorithm ◽  
Social Networks ◽  
Comparative Analysis ◽  
Community Detection ◽  
Quality Metrics

scholarly journals A Community-Based Approach for Link Prediction in Signed Social Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Saeed Reza Shahriary ◽  
Mohsen Shahriari ◽  
Rafidah MD Noor
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Link Prediction ◽  
Large Scale ◽  
Prediction Problem ◽  
Signed Graphs ◽  
Community Based ◽  
Ranking Algorithms ◽  
Detection Approach ◽  
Signed Social Networks

In signed social networks, relationships among nodes are of the types positive (friendship) and negative (hostility). One absorbing issue in signed social networks is predicting sign of edges among people who are members of these networks. Other than edge sign prediction, one can define importance of people or nodes in networks via ranking algorithms. There exist few ranking algorithms for signed graphs; also few studies have shown role of ranking in link prediction problem. Hence, we were motivated to investigate ranking algorithms availed for signed graphs and their effect on sign prediction problem. This paper makes the contribution of using community detection approach for ranking algorithms in signed graphs. Therefore, community detection which is another active area of research in social networks is also investigated in this paper. Community detection algorithms try to find groups of nodes in which they share common properties like similarity. We were able to devise three community-based ranking algorithms which are suitable for signed graphs, and also we evaluated these ranking algorithms via sign prediction problem. These ranking algorithms were tested on three large-scale datasets: Epinions, Slashdot, and Wikipedia. We indicated that, in some cases, these ranking algorithms outperform previous works because their prediction accuracies are better.

Stable Community Detection in Signed Social Networks

2020 ◽  
pp. 1-1
Author(s):  
Renjie Sun ◽  
Chen Chen ◽  
Xiaoyang Wang ◽  
Ying Zhang ◽  
Xun Wang
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Signed Social Networks ◽  
Stable Community
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