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.

scholarly journals An Improved Local Community Detection Algorithm Using Selection Probability

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Shixiong Xia ◽  
Ranran Zhou ◽  
Yong Zhou ◽  
Mu Zhu
Keyword(s):  
Community Detection ◽  
Local Community ◽  
Optimal Solution ◽  
Detection Algorithm ◽  
Local Communities ◽  
Selection Strategy ◽  
Selection Probability ◽  
Local Optimal Solution ◽  
Local Community Detection ◽  
Community Detection Algorithm

In order to find the structure of local community more effectively, we propose an improved local community detection algorithm ILCDSP, which improves the node selection strategy, and sets selection probability value for every candidate node. ILCDSP assigns nodes with different selection probability values, which are equal to the degree of the nodes to be chosen. By this kind of strategy, the proposed algorithm can detect the local communities effectively, since it can ensure the best search direction and avoid the local optimal solution. Various experimental results on both synthetic and real networks demonstrate that the quality of the local communities detected by our algorithm is significantly superior to the state-of-the-art methods.

scholarly journals Local Community Detection in Complex Networks Based on Maximum Cliques Extension

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Meng Fanrong ◽  
Zhu Mu ◽  
Zhou Yong ◽  
Zhou Ranran
Keyword(s):  
Complex Networks ◽  
Community Detection ◽  
Local Community ◽  
Empirical Evaluation ◽  
Local Communities ◽  
The State ◽  
Source Node ◽  
Superior Performance ◽  
Maximum Cliques ◽  
Local Community Detection

Detecting local community structure in complex networks is an appealing problem that has attracted increasing attention in various domains. However, most of the current local community detection algorithms, on one hand, are influenced by the state of the source node and, on the other hand, cannot effectively identify the multiple communities linked with the overlapping nodes. We proposed a novel local community detection algorithm based on maximum clique extension called LCD-MC. The proposed method firstly finds the set of all the maximum cliques containing the source node and initializes them as the starting local communities; then, it extends each unclassified local community by greedy optimization until a certain objective is satisfied; finally, the expected local communities will be obtained until all maximum cliques are assigned into a community. An empirical evaluation using both synthetic and real datasets demonstrates that our algorithm has a superior performance to some of the state-of-the-art approaches.

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
Sign in / Sign up

Export Citation Format

Share Document