Finding Overlapping Communities by Increasing the Determinacy of SLPA in Complex Networks

2018 ◽  
pp. 111-122 ◽  
Author(s):  
Jingyi Zhang ◽  
Zhixin Ma ◽  
Qijuan Sun ◽  
Jun Yan ◽  
Xiao Zhang ◽  
...  
Keyword(s):  
Complex Networks ◽  
Overlapping Communities

Detecting Overlapping Communities with MDS and Local Expansion FCM

2014 ◽  
Vol 644-650 ◽  
pp. 3295-3299
Author(s):  
Lin Li ◽  
Zheng Min Xia ◽  
Sheng Hong Li ◽  
Li Pan ◽  
Zhi Hua Huang
Keyword(s):  
Community Structure ◽  
Euclidean Space ◽  
Complex Networks ◽  
Test Results ◽  
Distance Information ◽  
Overlapping Communities ◽  
Expansion Strategy ◽  
Local Expansion ◽  
Structural And Functional Properties ◽  
Overlapping Community

Community structure is an important feature to understand structural and functional properties in various complex networks. In this paper, we use Multidimensional Scaling (MDS) to map nodes of network into Euclidean space to keep the distance information of nodes, and then we use topology feature of communities to propose the local expansion strategy to detect initial seeds for FCM. Finally, the FCM are used to uncover overlapping communities in the complex networks. The test results in real-world and artificial networks show that the proposed algorithm is efficient and robust in uncovering overlapping community structure.

Detection of Highly Overlapping Communities in Complex Networks

2015 ◽  
Vol 5 (5) ◽  
pp. 1099-1103 ◽  
Author(s):  
Madhusudan Paul ◽  
Rishav Anand ◽  
Ashish Anand
Keyword(s):  
Complex Networks ◽  
Overlapping Communities

Community cores expansion for overlapping community detection in complex networks

2018 ◽  
Vol 32 (33) ◽  
pp. 1850405 ◽  
Author(s):  
Yongjie Yan ◽  
Guang Yu ◽  
Xiangbin Yan ◽  
Hui Xie
Keyword(s):  
Complex Networks ◽  
Community Detection ◽  
Heuristic Algorithm ◽  
Real World ◽  
Local Structure ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Clique Percolation ◽  
Overlapping Community ◽  
Synthetic Datasets

The identification of communities has attracted considerable attentions in the last few years. We propose a novel heuristic algorithm for overlapping community detection based on community cores in complex networks. We introduce a novel clique percolation algorithm and maximize cliques in the finding overlapping communities (node covers) in graphs. We show how vertices can be used to quantify types of local structure presented in a community and identify group nodes that have similar roles in relation to their neighbors. We compare the approach with other three common algorithms in the analysis of the Zachary’s karate club network and the dolphins network. Experimental results in real-world and synthetic datasets (Lancichinetti–Fortunato–Radicchi (LFR) benchmark networks [A. Lancichinetti and S. Fortunato, Phys. Rev. E 80 (2009) 016118]) demonstrate the model has scalability and is well behaved.

ENTRAINMENT COMPETITION IN COMPLEX NETWORKS

2010 ◽  
Vol 20 (03) ◽  
pp. 827-833
Author(s):  
I. LEYVA ◽  
I. SENDIÑA-NADAL ◽  
J. A. ALMENDRAL ◽  
J. M. BULDÚ ◽  
D. LI ◽  
...  
Keyword(s):  
Complex Networks ◽  
Network Structure ◽  
New Method ◽  
Simultaneous Presence ◽  
Modular Network ◽  
Overlapping Communities ◽  
Frequency Changes

The response of a random and modular network to the simultaneous presence of two frequencies is considered. The competition for controlling the dynamics of the network results in different behaviors, such as frequency changes or permanent synchronization frustration, which can be directly related to the network structure. From these observations, we propose a new method for detecting overlapping communities in structured networks.

Detecting Overlapping Communities Based on Community Cores in Complex Networks

2010 ◽  
Vol 27 (5) ◽  
pp. 058901 ◽  
Author(s):  
Shang Ming-Sheng ◽  
Chen Duan-Bing ◽  
Zhou Tao
Keyword(s):  
Complex Networks ◽  
Overlapping Communities

scholarly journals A Markov random walk under constraint for discovering overlapping communities in complex networks

2011 ◽  
Vol 2011 (05) ◽  
pp. P05031 ◽  
Author(s):  
Di Jin ◽  
Bo Yang ◽  
Carlos Baquero ◽  
Dayou Liu ◽  
Dongxiao He ◽  
...  
Keyword(s):  
Random Walk ◽  
Complex Networks ◽  
Overlapping Communities ◽  
Markov Random ◽  
Markov Random Walk

A UNIFIED COMMUNITY DETECTION ALGORITHM IN LARGE-SCALE COMPLEX NETWORKS

2019 ◽  
Vol 22 (03) ◽  
pp. 1950004
Author(s):  
HAO LONG ◽  
XIAO-WEI LIU
Keyword(s):  
Complex Networks ◽  
Community Detection ◽  
Large Scale ◽  
Detection Algorithm ◽  
Component Structure ◽  
Overlapping Communities ◽  
Expansion Strategy ◽  
High Scalability ◽  
Community Detection Algorithm ◽  
Large Scale Networks

A community is the basic component structure of complex networks and is important for network analysis. In recent decades, researchers from different fields have witnessed a boom of community detection, and many algorithms were proposed to retrieve disjoint or overlapping communities. In this paper, a unified expansion approach is proposed to obtain two different network partitions, which can provide divisions with higher accuracies and have high scalability in large-scale networks. First, we define the edge intensity to quantify the densities of network edges, a higher edge intensity indicates a more compact pair of nodes. Second, vertices of higher density edges are extracted out and denoted as core nodes, whereas other vertices are treated as margin nodes; finally we apply an expansion strategy to form disjoint communities: closely connected core nodes are combined as disjoint skeleton communities, and margin nodes are gradually attached to the nearest skeleton communities. To detect overlapping communities, extra steps are adopted: potential overlapping nodes are identified from the existing disjoint communities and replicated; and communities that bear replicas are further partitioned into smaller clusters. Because replicas of potential overlapping nodes might remain in different communities, overlapping communities can be acquired. Experimental results on real and synthetic networks illustrate higher accuracy and better performance of our method.

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