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.

AGGLOMERATIVE CLUSTERING BASED ON LABEL PROPAGATION FOR DETECTING OVERLAPPING AND HIERARCHICAL COMMUNITIES IN COMPLEX NETWORKS

2014 ◽  
Vol 17 (06) ◽  
pp. 1450021 ◽  
Author(s):  
YUXIN ZHAO ◽  
SHENGHONG LI ◽  
SHILIN WANG
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Large Scale ◽  
Label Propagation ◽  
Agglomerative Clustering ◽  
Overlapping Communities ◽  
Small Communities ◽  
Structural And Functional Properties ◽  
Overlapping Community ◽  
Large Scale Networks

Community detection is an important issue to understand the structural and functional properties of complex networks, which still remains a challenging subject. In some complex networks, a node may belong to multiple communities, implying overlapping community structure. Moreover, complex networks often show a hierarchical structure where small communities group together to form larger ones. In this paper, we propose a novel parameter-free algorithm called agglomerative clustering based on label propagation algorithm (ACLPA) to detect both overlapping and hierarchical community structure in complex networks. By combining the advantages of agglomerative clustering and label propagation, our algorithm can build the hierarchical tree of overlapping communities in large-scale networks. The tests on both synthetic and real-world networks give excellent results and demonstrate the effectiveness and efficiency of our algorithm.

Overlapping Community Discovery Based on the Combination of Node Influence and β-Connected Neighbors

2019 ◽  
Vol 28 (04) ◽  
pp. 1950011
Author(s):  
Rongwang Chen ◽  
Qingshou Wu ◽  
Wenzhong Guo ◽  
Kun Guo ◽  
Qinze Wang
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Real World ◽  
Community Discovery ◽  
Overlapping Communities ◽  
The Core ◽  
Core Node ◽  
Comparison Methods ◽  
Overlapping Community

We propose an overlapping community discovery algorithm that combines node influence and [Formula: see text]-connected neighbors for effectively detecting the overlapping community structure of complex networks. On the basis of the node influence and [Formula: see text]-connected neighbors, our method accurately detects the core node community and uses the improved similarity between the node and community to expand the core node community. Accordingly, the discovery and optimization of network overlapping communities are realized. Experiments on artificial and real-world networks demonstrate that our method significantly and consistently outperforms other comparison methods.

An algorithm Walktrap-SPM for detecting overlapping community structure

2017 ◽  
Vol 31 (15) ◽  
pp. 1750121 ◽  
Author(s):  
Fang Hu ◽  
Youze Zhu ◽  
Yuan Shi ◽  
Jianchao Cai ◽  
Luogeng Chen ◽  
...  
Keyword(s):  
Community Structure ◽  
Random Walk ◽  
Community Detection ◽  
Real World ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Running Time ◽  
Overlapping Community ◽  
Negative Links ◽  
Novel Algorithm

In this paper, based on Walktrap algorithm with the idea of random walk, and by selecting the neighbor communities, introducing improved signed probabilistic mixture (SPM) model and considering the edges within the community as positive links and the edges between the communities as negative links, a novel algorithm Walktrap-SPM for detecting overlapping community is proposed. This algorithm not only can identify the overlapping communities, but also can greatly increase the objectivity and accuracy of the results. In order to verify the accuracy, the performance of this algorithm is tested on several representative real-world networks and a set of computer-generated networks based on LFR benchmark. The experimental results indicate that this algorithm can identify the communities accurately, and it is more suitable for overlapping community detection. Compared with Walktrap, SPM and LMF algorithms, the presented algorithm can acquire higher values of modularity and NMI. Moreover, this new algorithm has faster running time than SPM and LMF algorithms.

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.

scholarly journals A Local Overlapping Community Detection Method in Complex Networks

2012 ◽  
Vol 6-7 ◽  
pp. 985-990
Author(s):  
Yan Peng ◽  
Yan Min Li ◽  
Lan Huang ◽  
Long Ju Wu ◽  
Gui Shen Wang ◽  
...  
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Real World ◽  
Detection Method ◽  
Experimental Results ◽  
Overlapping Community Detection ◽  
Greedy Strategy ◽  
Structure Detection ◽  
Overlapping Community

Community structure detection has great importance in finding the relationships of elements in complex networks. This paper presents a method of simultaneously taking into account the weak community structure definition and community subgraph density, based on the greedy strategy for community expansion. The results are compared with several previous methods on artificial networks and real world networks. And experimental results verify the feasibility and effectiveness of our approach.

scholarly journals Centrality in Complex Networks with Overlapping Community Structure

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Zakariya Ghalmane ◽  
Chantal Cherifi ◽  
Hocine Cherifi ◽  
Mohammed El Hassouni
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Overlapping Community
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