Local Community Detection Using Social Relations and Topic Features in Social Networks

2017 ◽  
pp. 371-383
Author(s):  
Chengcheng Xu ◽  
Huaping Zhang ◽  
Bingbing Lu ◽  
Songze Wu
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Social Relations ◽  
Local Community ◽  
Local Community Detection

Optimal local community detection in social networks based on density drop of subgraphs

2014 ◽  
Vol 36 ◽  
pp. 46-53 ◽  
Author(s):  
Xingqin Qi ◽  
Wenliang Tang ◽  
Yezhou Wu ◽  
Guodong Guo ◽  
Eddie Fuller ◽  
...  
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Local Community ◽  
Local Community Detection

scholarly journals Approaching the Optimal Solution of the Maximal α-quasi-clique Local Community Problem

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1438
Author(s):  
Patricia Conde-Cespedes
Keyword(s):  
Graph Theory ◽  
Community Detection ◽  
Local Community ◽  
Optimal Solution ◽  
Local Communities ◽  
Optimal Value ◽  
Interesting Task ◽  
Local Community Detection ◽  
Networks Analysis ◽  
Node Problem

Complex networks analysis (CNA) has attracted so much attention in the last few years. An interesting task in CNA complex network analysis is community detection. In this paper, we focus on Local Community Detection, which is the problem of detecting the community of a given node of interest in the whole network. Moreover, we study the problem of finding local communities of high density, known as α-quasi-cliques in graph theory (for high values of α in the interval ]0,1[). Unfortunately, the higher α is, the smaller the communities become. This led to the maximal α-quasi-clique community of a given node problem, which is, the problem of finding local communities that are α-quasi-cliques of maximal size. This problem is NP-hard, then, to approach the optimal solution, some heuristics exist. When α is high (>0.5) the diameter of a maximal α-quasi-clique is at most 2. Based on this property, we propose an algorithm to calculate an upper bound to approach the optimal solution. We evaluate our method in real networks and conclude that, in most cases, the bound is very accurate. Furthermore, for a real small network, the optimal value is exactly achieved in more than 80% of cases.

Uncovering local community structure on line graph through degree centrality and expansion

2021 ◽  
pp. 2150120
Author(s):  
Guishen Wang ◽  
Kaitai Wang ◽  
Hongmei Wang ◽  
Huimin Lu ◽  
Xiaotang Zhou ◽  
...  
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Local Community ◽  
Line Graph ◽  
Detection Methods ◽  
Degree Centrality ◽  
Original Graph ◽  
Normalized Mutual Information ◽  
Local Community Detection ◽  
On Line

Local community detection algorithms are an important type of overlapping community detection methods. Local community detection methods identify local community structure through searching seeds and expansion process. In this paper, we propose a novel local community detection method on line graph through degree centrality and expansion (LCDDCE). We firstly employ line graph model to transfer edges into nodes of a new graph. Secondly, we evaluate edges relationship through a novel node similarity method on line graph. Thirdly, we introduce local community detection framework to identify local node community structure of line graph, combined with degree centrality and PageRank algorithm. Finally, we transfer them back into original graph. The experimental results on three classical benchmarks show that our LCDDCE method achieves a higher performance on normalized mutual information metric with other typical methods.

Local community detection with hints

2022 ◽  
Author(s):  
Georgia Baltsou ◽  
Konstantinos Tsichlas ◽  
Athena Vakali
Keyword(s):  
Community Detection ◽  
Local Community ◽  
Local Community Detection
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