Complex network community detection method by improved density peaks model

2019 ◽  
Vol 526 ◽  
pp. 121070 ◽  
Author(s):  
Zheng-Hong Deng ◽  
Hong-Hai Qiao ◽  
Ming-Yu Gao ◽  
Qun Song ◽  
Li Gao
Keyword(s):  
Complex Network ◽  
Community Detection ◽  
Detection Method ◽  
Network Community ◽  
Density Peaks

scholarly journals Multiobjective Brain Storm Optimization Community Detection Method Based on Novelty Search

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Xiaoying Pan ◽  
Jia Wang ◽  
Miao Wei ◽  
Hongye Li
Keyword(s):  
Community Structure ◽  
Complex Network ◽  
Community Detection ◽  
Detection Method ◽  
Premature Convergence ◽  
Practical Significance ◽  
Network Community ◽  
Brain Storm Optimization ◽  
Optimal Network ◽  
Novelty Search

A complex network is characterized by community structure, so it is of great theoretical and practical significance to discover hidden functions by detecting the community structure in complex networks. In this paper, a multiobjective brain storm optimization based on novelty search (MOBSO-NS) community detection method is proposed to solve the current issue of premature convergence caused by the loss of diversity in complex network community detection based on multiobjective optimization algorithm and improve the accuracy of community discovery. The proposed method designs a novel search strategy where novelty individuals are first constructed to improve the global search ability, thus avoiding falling into local optimal solutions; then, the objective space is divided into 3 clusters: elite cluster, ordinary cluster, and novel cluster, which are mapped to the decision space, and finally, the populations are disrupted and merged. In addition, the introduction of a restarting strategy is introduced to avoid stagnation by premature convergence. Experimental results show that the algorithm with good global searchability can find the Pareto optimal network community structure set with uniform distribution and high convergence and excavate the network community with higher quality.

scholarly journals LIMITED RESOLUTION AND MULTIRESOLUTION METHODS IN COMPLEX NETWORK COMMUNITY DETECTION

2007 ◽  
Vol 07 (03) ◽  
pp. L209-L214 ◽  
Author(s):  
JUSSI M. KUMPULA ◽  
JARI SARAMÄKI ◽  
KIMMO KASKI ◽  
JÁNOS KERTÉSZ
Keyword(s):  
Simulated Annealing ◽  
Community Structure ◽  
Complex Network ◽  
Community Detection ◽  
Real World ◽  
Tuning Parameter ◽  
Challenging Problem ◽  
Resolution Limit ◽  
Network Community ◽  
Multiresolution Methods

Detecting community structure in real-world networks is a challenging problem. Recently, it has been shown that the resolution of methods based on optimizing a modularity measure or a corresponding energy is limited; communities with sizes below some threshold remain unresolved. One possibility to go around this problem is to vary the threshold by using a tuning parameter, and investigate the community structure at variable resolutions. Here, we analyze the resolution limit and multiresolution behavior for two different methods: a q-state Potts method proposed by Reichard and Bornholdt, and a recent multiresolution method by Arenas, Fernández, and Gómez. These methods are studied analytically, and applied to three test networks using simulated annealing.

scholarly journals NCSS: an effective and efficient complex network community detection algorithm

2016 ◽  
Vol 46 (4) ◽  
pp. 431-444
Author(s):  
Zhongming HAN ◽  
Xusheng TAN ◽  
Yan CHEN ◽  
Dagao DUAN
Keyword(s):  
Complex Network ◽  
Community Detection ◽  
Detection Algorithm ◽  
Network Community ◽  
Community Detection Algorithm

scholarly journals Limited resolution in complex network community detection with Potts model approach

2007 ◽  
Vol 56 (1) ◽  
pp. 41-45 ◽  
Author(s):  
J. M. Kumpula ◽  
J. Saramäki ◽  
K. Kaski ◽  
J. Kertész
Keyword(s):  
Complex Network ◽  
Community Detection ◽  
Potts Model ◽  
Network Community ◽  
Model Approach
Sign in / Sign up

Export Citation Format

Share Document