A Unified Framework of Lightweight Local Community Detection for Different Node Similarity Measurement

2017 ◽  
pp. 283-295 ◽  
Author(s):  
Jinglian Liu ◽  
Daling Wang ◽  
Weiji Zhao ◽  
Shi Feng ◽  
Yifei Zhang
Keyword(s):  
Community Detection ◽  
Local Community ◽  
Similarity Measurement ◽  
Unified Framework ◽  
Node Similarity ◽  
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

scholarly journals Multiple Local Community Detection

2018 ◽  
Vol 45 (3) ◽  
pp. 76-83 ◽  
Author(s):  
Alexandre Hollocou ◽  
Thomas Bonald ◽  
Marc Lelarge
Keyword(s):  
Community Detection ◽  
Local Community ◽  
Local Community Detection

Local community detection based on modularity metric G

2015 ◽  
Vol 29 (33) ◽  
pp. 1550215 ◽  
Author(s):  
Zhengyou Xia ◽  
Xiangying Gao ◽  
Xia Zhang
Keyword(s):  
Network Analysis ◽  
Community Detection ◽  
World Wide ◽  
Local Community ◽  
Complete Information ◽  
Detection Problem ◽  
Two Phase ◽  
The World ◽  
Local Community Detection

In complex network analysis, the local community detection problem is getting more and more attention. Because of the difficulty to get complete information of the network, such as the World Wide Web, the local community detection has been proposed by researcher. That is, we can detect a community from a certain source vertex with limited knowledge of an entire graph. The previous methods of local community detection now are more or less inadequate in some places. In this paper, we have proposed a new local modularity metric [Formula: see text] and based on it, a two-phase algorithm is proposed. The method we have taken is a greedy addition algorithm which means adding vertices into the community until [Formula: see text] does not increase. Compared with the previous methods, when our method is calculating the modularity metric, the range of vertices what we considered may affect the quality of the community detection wider. The results of experiments show that whether in computer-generated random graph or in the real networks, our method can effectively solve the problem of the local community detection.

Memory-based random walk for multi-query local community detection

2019 ◽  
Vol 62 (5) ◽  
pp. 2067-2101
Author(s):  
Yuchen Bian ◽  
Dongsheng Luo ◽  
Yaowei Yan ◽  
Wei Cheng ◽  
Wei Wang ◽  
...  
Keyword(s):  
Random Walk ◽  
Community Detection ◽  
Local Community ◽  
Local Community Detection
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