Journal ArticleDOI
Detecting overlapping communities by seed community in weighted complex networks
TLDR
This work presents a unique algorithm to detect overlapping communities in the weighted complex networks with considerable accuracy and successfully finds common nodes between communities.Abstract:
Detection of community structures in the weighted complex networks is significant to understand the network structures and analysis of the network properties. We present a unique algorithm to detect overlapping communities in the weighted complex networks with considerable accuracy. For a given weighted network, all the seed communities are first extracted. Then to each seed community, more community members are absorbed using the absorbing degree function. In addition, our algorithm successfully finds common nodes between communities. The experiments using some real-world networks show that the performance of our algorithm is satisfactory.read more
Citations
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Journal ArticleDOI
A Novel Method for Detecting New Overlapping Community in Complex Evolving Networks
TL;DR: This paper presents a novel method based on node vitality, an extension of node fitness for modeling network evolution constrained by multiscaling and preferential attachment, and shows how to detect an overlapping community in a real-world evolving network.
Journal ArticleDOI
GMM: A generalized mechanics model for identifying the importance of nodes in complex networks
Fan Liu,Zhen Wang,Yong Deng +2 more
TL;DR: An innovative experimental network-based quality assessment was proposed to validate the method of identifying the importance of nodes and a generalized mechanical model is proposed that uses global information and local information.
Journal ArticleDOI
Detecting and refining overlapping regions in complex networks with three-way decisions
TL;DR: A three-way representation of a community is given by using interval sets and re-formalize the problem of community detection as three- way clustering to investigate methods that not only detect the overlapping communities but also refine the overlapping regions.
Journal ArticleDOI
Overlapping community detection based on node location analysis
TL;DR: In this paper, a new overlapping community detection method based on node location analysis is proposed, using the PageRank algorithm to evaluate the node mass, and the community affiliation of nodes is determined based on their positions in the inherent peak-valley structure of the topology potential field.
Journal ArticleDOI
Tracking the evolution of overlapping communities in dynamic social networks
TL;DR: A novel Dynamic Overlapping Community Evolution Tracking method to solve the three problems simultaneously with one single model, i.e. topology potential field, which can both accurately partition dynamic overlapping social networks and efficiently track all kinds of community evolution events.
References
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Journal ArticleDOI
Statistical mechanics of complex networks
TL;DR: In this paper, a simple model based on the power-law degree distribution of real networks was proposed, which was able to reproduce the power law degree distribution in real networks and to capture the evolution of networks, not just their static topology.
Journal ArticleDOI
The Structure and Function of Complex Networks
TL;DR: Developments in this field are reviewed, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
Journal ArticleDOI
Community structure in social and biological networks
Michelle Girvan,Mark Newman +1 more
TL;DR: This article proposes a method for detecting communities, built around the idea of using centrality indices to find community boundaries, and tests it on computer-generated and real-world graphs whose community structure is already known and finds that the method detects this known structure with high sensitivity and reliability.
Journal ArticleDOI
Fast unfolding of communities in large networks
Vincent D. Blondel,Jean-Loup Guillaume,Jean-Loup Guillaume,Renaud Lambiotte,Renaud Lambiotte,Etienne Lefebvre +5 more
TL;DR: This work proposes a heuristic method that is shown to outperform all other known community detection methods in terms of computation time and the quality of the communities detected is very good, as measured by the so-called modularity.
Journal ArticleDOI
Finding and evaluating community structure in networks.
TL;DR: It is demonstrated that the algorithms proposed are highly effective at discovering community structure in both computer-generated and real-world network data, and can be used to shed light on the sometimes dauntingly complex structure of networked systems.