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 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.

A fast local community detection algorithm in complex networks

2021 ◽  
Author(s):  
Zhikang Tang ◽  
Yong Tang ◽  
Chunying Li ◽  
Jinli Cao ◽  
Guohua Chen ◽  
...  
Keyword(s):  
Complex Networks ◽  
Community Detection ◽  
Local Community ◽  
Detection Algorithm ◽  
Local Community Detection ◽  
Community Detection Algorithm

scholarly journals Local Community Detection Algorithm Based on Minimal Cluster

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Yong Zhou ◽  
Guibin Sun ◽  
Yan Xing ◽  
Ranran Zhou ◽  
Zhixiao Wang
Keyword(s):  
Community Detection ◽  
Local Community ◽  
Detection Algorithm ◽  
Single Node ◽  
Initial Node ◽  
Detection Algorithms ◽  
Local Community Detection ◽  
Two Phases ◽  
Community Detection Algorithm

In order to discover the structure of local community more effectively, this paper puts forward a new local community detection algorithm based on minimal cluster. Most of the local community detection algorithms begin from one node. The agglomeration ability of a single node must be less than multiple nodes, so the beginning of the community extension of the algorithm in this paper is no longer from the initial node only but from a node cluster containing this initial node and nodes in the cluster are relatively densely connected with each other. The algorithm mainly includes two phases. First it detects the minimal cluster and then finds the local community extended from the minimal cluster. Experimental results show that the quality of the local community detected by our algorithm is much better than other algorithms no matter in real networks or in simulated networks.

scholarly journals DLCD-CCE: A Local Community Detection Algorithm for Complex IoT Networks

2020 ◽  
Vol 7 (5) ◽  
pp. 4607-4615 ◽  
Author(s):  
Xiaolong Xu ◽  
Nan Hu ◽  
Marcello Trovati ◽  
Jeffrey Ray ◽  
Francesco Palmieri ◽  
...  
Keyword(s):  
Community Detection ◽  
Local Community ◽  
Detection Algorithm ◽  
Local Community Detection ◽  
Community Detection Algorithm
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