Journal ArticleDOI
Community Discovery Method in Networks Based on Topological Potential: Community Discovery Method in Networks Based on Topological Potential
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This article is published in Journal of Software.The article was published on 2009-11-13. It has received 53 citations till now.read more
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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
Identifying critical nodes in metro network considering topological potential: A case study in Shenzhen city-China
TL;DR: It is found that ITPE method could effectively identify nodes or stations which are crucial both on network structure and passenger flow mobility while traditional undirected and unweighted network cannot completely identify.
Proceedings ArticleDOI
A Method for Local Community Detection by Finding Core Nodes
Tiantian Zhang,Bin Wu +1 more
TL;DR: This paper proposes a method to detect local community of a given node by finding the core node of the community firstly and expanding the core nodes' cliques to get community of the given node.
Journal ArticleDOI
Finding overlapping community from social networks based on community forest model
TL;DR: A novel overlapping community detection algorithm named CFM has better performance than MMSB, Louvain method and CPM, and is proposed to give a clear formula definition of overlapping community and disjoint community based on the backbone degree and expansion.
Journal ArticleDOI
Multi-objective community detection algorithm with node importance analysis in attributed networks
TL;DR: A novel Multi-objective Attributed Attributed community detection algorithm with Node Importance Analysis (MANIA) that detects more meaningful and interpretable communities and significantly outperforms the rivals.
References
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Journal ArticleDOI
Collective dynamics of small-world networks
TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
Journal ArticleDOI
Emergence of Scaling in Random Networks
TL;DR: A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
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
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.