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.

Detecting overlapping communities using ensemble-based distributed neighbourhood threshold method in social networks

2021 ◽  
pp. 1-17
Author(s):  
Rajesh Jaiswal ◽  
Sheela Ramanna
Keyword(s):  
Community Detection ◽  
Real World ◽  
Detection Algorithm ◽  
Threshold Method ◽  
Boundary Node ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Starting Point ◽  
Overlapping Community ◽  
Real World Datasets

In this paper, we have proposed a novel overlapping community detection algorithm based on an ensemble approach with a distributed neighbourhood threshold method (EnDNTM). EnDNTM uses pre-partitioned disjoint communities generated by the ensemble mechanism and then analyzes the neighbourhood distribution of boundary nodes in disjoint communities to detect overlapping communities. It is a form of seed-based global method since boundary nodes are considered as seeds and become the starting point for detecting overlapping communities. A threshold value for each boundary node is used as the minimum influence by the neighbours of a node in order to determine its belongingness to any community. The effectiveness of the EnDNTM algorithm has been demonstrated by testing with five synthetic benchmark datasets and fifteen real-world datasets. The performance of the EnDNTM algorithm was compared with seven overlapping community detection algorithms. The F1-score, normalized mutual information ONMI and extended modularity Qo⁢v metrics were used to measure the quality of the detected communities. EnDNTM outperforms comparable algorithms on 4 out of 5 synthetic benchmarks datasets, 11 out of 15 real world datasets and gives comparable results with the remaining datasets. Experiments on various synthetic and real world datasets reveal that for a majority of datasets, the proposed ensemble-based distributed neighbourhood threshold method is able to select the best disjoint clusters produced by a disjoint method from a collection of methods for detecting overlapping communities.

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.

scholarly journals Overlapping Community Detection Based on Membership Degree Propagation

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 15
Author(s):  
Rui Gao ◽  
Shoufeng Li ◽  
Xiaohu Shi ◽  
Yanchun Liang ◽  
Dong Xu
Keyword(s):  
Complex Network ◽  
Community Detection ◽  
Real World ◽  
Detection Algorithm ◽  
Label Propagation ◽  
Computational Time ◽  
Membership Degree ◽  
Overlapping Community Detection ◽  
Overlapping Community ◽  
Real World Datasets

A community in a complex network refers to a group of nodes that are densely connected internally but with only sparse connections to the outside. Overlapping community structures are ubiquitous in real-world networks, where each node belongs to at least one community. Therefore, overlapping community detection is an important topic in complex network research. This paper proposes an overlapping community detection algorithm based on membership degree propagation that is driven by both global and local information of the node community. In the method, we introduce a concept of membership degree, which not only stores the label information, but also the degrees of the node belonging to the labels. Then the conventional label propagation process could be extended to membership degree propagation, with the results mapped directly to the overlapping community division. Therefore, it obtains the partition result and overlapping node identification simultaneously and greatly reduces the computational time. The proposed algorithm was applied to a synthetic Lancichinetti–Fortunato–Radicchi (LFR) dataset and nine real-world datasets and compared with other up-to-date algorithms. The experimental results show that our proposed algorithm is effective and outperforms the comparison methods on most datasets. Our proposed method significantly improved the accuracy and speed of the overlapping node prediction. It can also substantially alleviate the computational complexity of community structure detection in general.

scholarly journals Overlapping Community Detection of Bipartite Networks Based on a Novel Community Density

2021 ◽  
Vol 13 (4) ◽  
pp. 89
Author(s):  
Yubo Peng ◽  
Bofeng Zhang ◽  
Furong Chang
Keyword(s):  
Community Detection ◽  
Real World ◽  
Detection Algorithm ◽  
Point Of View ◽  
Bipartite Network ◽  
Bipartite Networks ◽  
Overlapping Community Detection ◽  
The Real ◽  
Overlapping Community ◽  
Community Detection Algorithm

Community detection plays an essential role in understanding network topology and mining underlying information. A bipartite network is a complex network with more important authenticity and applicability than a one-mode network in the real world. There are many communities in the network that present natural overlapping structures in the real world. However, most of the research focuses on detecting non-overlapping community structures in the bipartite network, and the resolution of the existing evaluation function for the community structure’s merits are limited. So, we propose a novel function for community detection and evaluation of the bipartite network, called community density D. And based on community density, a bipartite network community detection algorithm DSNE (Density Sub-community Node-pair Extraction) is proposed, which is effective for overlapping community detection from a micro point of view. The experiments based on artificially-generated networks and real-world networks show that the DSNE algorithm is superior to some existing excellent algorithms; in comparison, the community density (D) is better than the bipartite network’s modularity.

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.

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.

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.

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