scholarly journals Overlapping Community Detection Extended from Disjoint Community Structure

2019 ◽  
Vol 38 (5) ◽  
pp. 1091-1110
Author(s):  
Yan Xing ◽  
Fanrong Meng ◽  
Yong Zhou ◽  
Guibin Sun ◽  
Zhixiao Wang
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Overlapping Community Detection ◽  
Overlapping Community

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.

A Novel Overlapping Community Detection Algorithm of Weighted Networks

2014 ◽  
Vol 599-601 ◽  
pp. 1369-1373
Author(s):  
Huang Bin You ◽  
Xue Wu Zhang ◽  
Huai Yong Fu ◽  
Zhuo Zhang ◽  
Min Li ◽  
...  
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Classification Accuracy ◽  
Time Complexity ◽  
Maximum Degree ◽  
Detection Algorithm ◽  
Overlapping Community Detection ◽  
Weighted Networks ◽  
Overlapping Community ◽  
Community Detection Algorithm

The community structure is a vital property of complex networks. As special networks the weighted networks also have community structure. Nowadays the studies of overlapping community draw attentions of researchers. However, the scale of networks become huge, so it requires the algorithm has lower time complexity and higher classification accuracy. Many existing algorithms cannot meet these two requirements at the same time. So we propose a novel overlapping community detection algorithm. Firstly we apply maximum degree node and its some special adjacent nodes as the initial community, and then expand the initial community by adding eligible nodes to it, finally other communities can be found by repeating these two steps. Experiments results show that our algorithm can detect overlapping community structure from weighted networks successfully, and also reveal that our method has higher division accuracy and lower time complexity than many previously proposed methods.

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.

Overlapping community detection based on link graph using distance dynamics

2018 ◽  
Vol 32 (03) ◽  
pp. 1850015 ◽  
Author(s):  
Lei Chen ◽  
Jing Zhang ◽  
Li-Jun Cai
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Dynamic Interaction ◽  
Interaction Process ◽  
Second Phase ◽  
Dynamics Model ◽  
Original Graph ◽  
Overlapping Community Detection ◽  
Edge Graph ◽  
Overlapping Community

The distance dynamics model was recently proposed to detect the disjoint community of a complex network. To identify the overlapping structure of a network using the distance dynamics model, an overlapping community detection algorithm, called L-Attractor, is proposed in this paper. The process of L-Attractor mainly consists of three phases. In the first phase, L-Attractor transforms the original graph to a link graph (a new edge graph) to assure that one node has multiple distances. In the second phase, using the improved distance dynamics model, a dynamic interaction process is introduced to simulate the distance dynamics (shrink or stretch). Through the dynamic interaction process, all distances converge, and the disjoint community structure of the link graph naturally manifests itself. In the third phase, a recovery method is designed to convert the disjoint community structure of the link graph to the overlapping community structure of the original graph. Extensive experiments are conducted on the LFR benchmark networks as well as real-world networks. Based on the results, our algorithm demonstrates higher accuracy and quality than other state-of-the-art algorithms.

scholarly journals Overlapping Community Detection Based on Node Importance and Adjacency Information

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Ping Wang ◽  
Yonghong Huang ◽  
Fei Tang ◽  
Hongtao Liu ◽  
Yangyang Lu
Keyword(s):  
Community Structure ◽  
Social Network ◽  
Community Detection ◽  
Evaluation Method ◽  
Detection Algorithm ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Node Importance ◽  
Overlapping Community ◽  
Community Detection Algorithm

Detecting the community structure and predicting the change of community structure is an important research topic in social network research. Focusing on the importance of nodes and the importance of their neighbors and the adjacency information, this article proposes a new evaluation method of node importance. The proposed overlapping community detection algorithm (ILE) uses the random walk to select the initial community and adopts the adaptive function to expand the community. It finally optimizes the community to obtain the overlapping community. For the overlapping communities, this article analyzes the evolution of networks at different times according to the stability and differences of social networks. Seven common community evolution events are obtained. The experimental results show that our algorithm is feasible and capable of discovering overlapping communities in complex social network efficiently.

scholarly journals Overlapping Community Detection Based on Attribute Augmented Graph

Entropy ◽  
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.

Overlapping community detection combining content and link

2012 ◽  
Vol 13 (11) ◽  
pp. 828-839 ◽  
Author(s):  
Zhou-zhou He ◽  
Zhong-fei Mark Zhang ◽  
Philip S. Yu
Keyword(s):  
Community Detection ◽  
Overlapping Community Detection ◽  
Overlapping Community
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