scholarly journals Overlapping community detection in networks based on link partitioning and partitioning around medoids

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255717
Author(s):  
Alexander Ponomarenko ◽  
Leonidas Pitsoulis ◽  
Marat Shamshetdinov
Keyword(s):  
Line Graph ◽  
Real Life ◽  
Distance Functions ◽  
Medium Size ◽  
Number Of Clusters ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Partitioning Around Medoids ◽  
Overlapping Community ◽  
Commute Distance

In this paper, we present a new method for detecting overlapping communities in networks with a predefined number of clusters called LPAM (Link Partitioning Around Medoids). The overlapping communities in the graph are obtained by detecting the disjoint communities in the associated line graph employing link partitioning and partitioning around medoids which are done through the use of a distance function defined on the set of nodes. We consider both the commute distance and amplified commute distance as distance functions. The performance of the LPAM method is evaluated with computational experiments on real life instances, as well as synthetic network benchmarks. For small and medium-size networks, the exact solution was found, while for large networks we found solutions with a heuristic version of the LPAM method.

An overlapping community detection algorithm based on node distance of line graph

2019 ◽  
Vol 33 (26) ◽  
pp. 1950322 ◽  
Author(s):  
Guishen Wang ◽  
Yuanwei Wang ◽  
Kaitai Wang ◽  
Zhihua Liu ◽  
Lijuan Zhang ◽  
...  
Keyword(s):  
Community Detection ◽  
Clustering Algorithm ◽  
Line Graph ◽  
Detection Algorithm ◽  
Density Peak ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Normalized Mutual Information ◽  
Overlapping Community ◽  
Community Detection Algorithm

Overlapping community detection is a hot topic in research of complex networks. Link community detection is a popular approach to discover overlapping communities. Line graph is a widely used model in link community detection. In this paper, we propose an overlapping community detection algorithm based on node distance of line graph. Considering topological structure of links in graphs, we use line graph to transform links of graph into nodes of line graph. Then, we calculate node distance of line graph according to their dissimilarity. After getting distance matrix, we proposed a new [Formula: see text] measure based on nodes of line graph and combine it with clustering algorithm by fast search and density peak to identify node communities of line graph. Finally, we acquire overlapping node communities after transforming node communities of line graph back to graph. The experimental results show that our algorithm achieves a higher performance on normalized mutual information metric.

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.

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.

Erratum: An overlapping community detection algorithm based on node distance of line graph

2019 ◽  
Vol 33 (30) ◽  
pp. 1992001
Author(s):  
Guishen Wang ◽  
Yuanwei Wang ◽  
Kaitai Wang ◽  
Zhihua Liu ◽  
Lijuan Zhang ◽  
...  
Keyword(s):  
Community Detection ◽  
Line Graph ◽  
Detection Algorithm ◽  
Overlapping Community Detection ◽  
Overlapping Community ◽  
Community Detection Algorithm

scholarly journals Evaluating the role of community detection in improving influence maximization heuristics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
László Hajdu ◽  
Miklós Krész ◽  
András Bóta
Keyword(s):  
Community Detection ◽  
Real Life ◽  
Detection Algorithm ◽  
Influence Maximization ◽  
Overlapping Community Detection ◽  
Detection Algorithms ◽  
Overlapping Community ◽  
Community Detection Algorithm ◽  
The Cost

AbstractBoth community detection and influence maximization are well-researched fields of network science. Here, we investigate how several popular community detection algorithms can be used as part of a heuristic approach to influence maximization. The heuristic is based on the community value, a node-based metric defined on the outputs of overlapping community detection algorithms. This metric is used to select nodes as high influence candidates for expanding the set of influential nodes. Our aim in this paper is twofold. First, we evaluate the performance of eight frequently used overlapping community detection algorithms on this specific task to show how much improvement can be gained compared to the originally proposed method of Kempe et al. Second, selecting the community detection algorithm(s) with the best performance, we propose a variant of the influence maximization heuristic with significantly reduced runtime, at the cost of slightly reduced quality of the output. We use both artificial benchmarks and real-life networks to evaluate the performance of our approach.

scholarly journals Multilayer Social Network Overlapping Community Detection Algorithm Based on Trust Relationship

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Junjie Jia ◽  
Pengtao Liu ◽  
Xiaojin Du ◽  
Yuchao Zhang
Keyword(s):  
Social Network ◽  
Community Detection ◽  
Detection Algorithm ◽  
Experimental Comparison ◽  
Overlapping Community Detection ◽  
Trust Value ◽  
Overlapping Communities ◽  
Trust Relationship ◽  
Overlapping Community ◽  
Community Detection Algorithm

Aiming at the problem of the lack of user social attribute characteristics in the process of dividing overlapping communities in multilayer social networks, in this paper, we propose a multilayer social network overlapping community detection algorithm based on trust relationship. By combining structural trust and social attribute trust, we transform a complex multilayer social network into a single-layer trust network. We obtain the community structure according to the community discovery algorithm based on trust value and merge communities with higher overlap. The experimental comparison and analysis are carried out on the synthetic network and the real network, respectively. The experimental results show that the proposed algorithm has higher harmonic mean and modularity than other algorithms of the same type.

Link community detection based on ensemble learning

2020 ◽  
Vol 34 (27) ◽  
pp. 2050293
Author(s):  
Zhihua Liu ◽  
Hongmei Wang ◽  
Guishen Wang ◽  
Yu Zhou
Keyword(s):  
Ensemble Learning ◽  
Community Detection ◽  
Clustering Algorithm ◽  
Line Graph ◽  
Distance Matrix ◽  
Original Graph ◽  
Overlapping Communities ◽  
Normalized Mutual Information ◽  
Pca Method ◽  
Overlapping Community

Overlapping community detection is a hot topic in the research of data mining and graph theory. In this paper, we propose a link community detection method based on ensemble learning (LCDEL). First, we transform graph into line graph and construct node adjacency matrix of line graph. Second, we calculate node distance of line graph through a new distance metric and get node distance matrix of line graph. Third, we use PCA method to reduce dimensions of node distance matrix of line graph. Then, we cluster on the reduced node distance matrix by k-means clustering algorithm. Finally, we convert line graph back into original graph and get overlapping communities of original graph with ensemble learning. Experimental results on several real-world networks demonstrate effectiveness of LCDEL method in terms of Normalized Mutual Information (NMI), Extended Modularity (EQ) and F-score evaluation metrics.

A non-binary hierarchical tree overlapping community detection based on multi-dimensional similarity

2021 ◽  
Vol 25 (5) ◽  
pp. 1099-1113
Author(s):  
Jie Chen ◽  
Huijun Wang ◽  
Shu Zhao ◽  
Ying Wang ◽  
Yanping Zhang
Keyword(s):  
Community Detection ◽  
Binary Tree ◽  
Structural Information ◽  
Similarity Matrix ◽  
Hierarchical Tree ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Overlapping Community ◽  
Principle Of Maximum

Overlapping communities exist in real networks, where the communities represent hierarchical community structures, such as schools and government departments. A non-binary tree allows a vertex to belong to multiple communities to obtain a more realistic overlapping community structure. It is challenging to select appropriate leaf vertices and construct a hierarchical tree that considers a large amount of structural information. In this paper, we propose a non-binary hierarchical tree overlapping community detection based on multi-dimensional similarity. The multi-dimensional similarity fully considers the local structure characteristics between vertices to calculate the similarity between vertices. First, we construct a similarity matrix based on the first and second-order neighbor vertices and select a leaf vertex. Second, we expand the leaf vertex based on the principle of maximum community density and construct a non-binary tree. Finally, we choose the layer with the largest overlapping modularity as the result of community division. Experiments on real-world networks demonstrate that our proposed algorithm is superior to other representative algorithms in terms of the quality of overlapping community detection.

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.

A Survey on Overlapping Communities in Large-Scale Social Networks

2018 ◽  
pp. 198-215
Author(s):  
S Rao Chintalapudi ◽  
H. M. Krishna Prasad M
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Biological Networks ◽  
Modern World ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Detection Algorithms ◽  
Research Areas ◽  
Network Generator ◽  
Overlapping Community

Social network analysis is one of the emerging research areas in the modern world. Social networks can be adapted to all the sectors by using graph theory concepts such as transportation networks, collaboration networks, and biological networks and so on. The most important property of social networks is community, collection of nodes with dense connections inside and sparse connections at outside. Community detection is similar to clustering analysis and has many applications in the real-time world such as recommendation systems, target marketing and so on. Community detection algorithms are broadly classified into two categories. One is disjoint community detection algorithms and the other is overlapping community detection algorithms. This chapter reviews overlapping community detection algorithms with their strengths and limitations. To evaluate these algorithms, a popular synthetic network generator, i.e., LFR benchmark generator and the new extended quality measures are discussed in detail.

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