scholarly journals Detecting Local Community Structures in Networks Based on Boundary Identification

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Liu Yang ◽  
Ji Xin-sheng ◽  
Liu Caixia ◽  
Wang Ding
Keyword(s):  
Community Detection ◽  
Real World ◽  
Local Community ◽  
Adaptive Method ◽  
Global Network ◽  
World Systems ◽  
Internal Link ◽  
Local Community Detection ◽  
Stability And Accuracy ◽  
Self Adaptive

Detecting communities within networks is of great importance to understand the structure and organizations of real-world systems. To this end, one of the major challenges is to find the local community from a given node with limited knowledge of the global network. Most of the existing methods largely depend on the starting node and require predefined parameters to control the agglomeration procedure, which may cause disturbing inference to the results of local community detection. In this work, we propose a parameter-free local community detecting algorithm, which uses two self-adaptive phases in detecting the local community, thus comprehensively considering the external and internal link similarity of neighborhood nodes in each clustering iteration. Based on boundary nodes identification, our self-adaptive method can effectively control the scale and scope of the local community. Experimental results show that our algorithm is efficient and well-behaved in both computer-generated and real-world networks, greatly improving the performance of local community detection in terms of stability and accuracy.

scholarly journals Multiple Local Community Detection via High-Quality Seed Identification over Both Static and Dynamic Networks

2021 ◽  
Author(s):  
Jiaxu Liu ◽  
Yingxia Shao ◽  
Sen Su
Keyword(s):  
Community Detection ◽  
Real World ◽  
Local Community ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
High Quality ◽  
Seed Node ◽  
Detection Algorithms ◽  
Local Community Detection ◽  
High Quality Seed

AbstractLocal community detection aims to find the communities that a given seed node belongs to. Most existing works on this problem are based on a very strict assumption that the seed node only belongs to a single community, but in real-world networks, nodes are likely to belong to multiple communities. In this paper, we first introduce a novel algorithm, HqsMLCD, that can detect multiple communities for a given seed node over static networks. HqsMLCD first finds the high-quality seeds which can detect better communities than the given seed node with the help of network representation, then expands the high-quality seeds one-by-one to get multiple communities, probably overlapping. Since dynamic networks also act an important role in practice, we extend the static HqsMLCD to handle dynamic networks and introduce HqsDMLCD. HqsDMLCD mainly integrates dynamic network embedding and dynamic local community detection into the static one. Experimental results on real-world networks demonstrate that our new method HqsMLCD outperforms the state-of-the-art multiple local community detection algorithms. And our dynamic method HqsDMLCD gets comparable results with the static method on real-world networks.

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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