A Local Overlapping Community Detection Method in Complex Networks

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.

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An algorithm Walktrap-SPM for detecting overlapping community structure

International Journal of Modern Physics B ◽

10.1142/s0217979217501211 ◽

2017 ◽

Vol 31 (15) ◽

pp. 1750121 ◽

Cited By ~ 1

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.

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Community cores expansion for overlapping community detection in complex networks

Modern Physics Letters B ◽

10.1142/s0217984918504055 ◽

2018 ◽

Vol 32 (33) ◽

pp. 1850405 ◽

Cited By ~ 2

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.

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Using functional dependency and node closure to detect communities from the sparse network

International Journal of Modern Physics B ◽

10.1142/s0217979218500182 ◽

2018 ◽

Vol 32 (03) ◽

pp. 1850018

Author(s):

Yang Zhou ◽

Haifei Miao ◽

Wei Liu ◽

Xiaoyun Chen ◽

Jianjun Cheng

Keyword(s):

Community Structure ◽

Complex Networks ◽

Community Detection ◽

Real World ◽

Time Complexity ◽

Functional Dependency ◽

Experimental Results ◽

Time Consumption ◽

Database Theory ◽

Effectiveness And Efficiency

Community structure is one of the most important features of complex networks, a large number of methods have been proposed to extract community structures from networks. However, some of those methods suffer from the high time complexity, and some of them cannot obtain the acceptable results. In this paper, we borrow the idea from the database theory, and propose the concepts of functional dependency (FD) between nodes and node closure first, then we utilize these concepts to extract communities. This method takes both effectiveness and efficiency into consideration, the community detection process can be accomplished with O(m) time consumption. We conducted extensive experiments both on some synthetic networks and on some real-world networks, the experimental results demonstrate that the method can detect communities from a given network successfully.

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Overlapping Community Detection Based on Attribute Augmented Graph

Entropy ◽

10.3390/e23060680 ◽

2021 ◽

Vol 23 (6) ◽

pp. 680

Author(s):

Hanyang Lin ◽

Yongzhao Zhan ◽

Zizheng Zhao ◽

Yuzhong Chen ◽

Chen Dong

Keyword(s):

Community Detection ◽

Real World ◽

Detection Algorithm ◽

Overlapping Community Detection ◽

Overlapping Communities ◽

Adjustment Strategy ◽

Topology Information ◽

Overlapping Community ◽

Real World Datasets ◽

Community Detection Algorithm

There is a wealth of information in real-world social networks. In addition to the topology information, the vertices or edges of a social network often have attributes, with many of the overlapping vertices belonging to several communities simultaneously. It is challenging to fully utilize the additional attribute information to detect overlapping communities. In this paper, we first propose an overlapping community detection algorithm based on an augmented attribute graph. An improved weight adjustment strategy for attributes is embedded in the algorithm to help detect overlapping communities more accurately. Second, we enhance the algorithm to automatically determine the number of communities by a node-density-based fuzzy k-medoids process. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively detect overlapping communities with fewer parameters compared to the baseline methods.

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