LED: A fast overlapping communities detection algorithm based on structural clustering

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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A novel dynamical community detection algorithm based on weighting scheme

International Journal of Modern Physics C ◽

10.1142/s0129183115500916 ◽

2015 ◽

Vol 26 (08) ◽

pp. 1550091 ◽

Cited By ~ 4

Author(s):

Ju Li ◽

Kai Yu ◽

Ke Hu

Keyword(s):

Detection Algorithm ◽

Weighting Scheme ◽

Overlapping Communities ◽

Structure Detection ◽

Stop Time ◽

Markov Random Walk ◽

Link Strength ◽

Community Detection Algorithm ◽

Auto Covariance ◽

Membership Vector

Network dynamics plays an important role in analyzing the correlation between the function properties and the topological structure. In this paper, we propose a novel dynamical iteration (DI) algorithm, which incorporates the iterative process of membership vector with weighting scheme, i.e. weighting W and tightness T. These new elements can be used to adjust the link strength and the node compactness for improving the speed and accuracy of community structure detection. To estimate the optimal stop time of iteration, we utilize a new stability measure which is defined as the Markov random walk auto-covariance. We do not need to specify the number of communities in advance. It naturally supports the overlapping communities by associating each node with a membership vector describing the node's involvement in each community. Theoretical analysis and experiments show that the algorithm can uncover communities effectively and efficiently.

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Multilayer Social Network Overlapping Community Detection Algorithm Based on Trust Relationship

Wireless Communications and Mobile Computing ◽

10.1155/2021/9268039 ◽

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.

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Gravitational community detection by predicting diameter

Discrete Mathematics Algorithms and Applications ◽

10.1142/s1793830921501457 ◽

2021 ◽

pp. 2150145

Author(s):

Himansu Sekhar Pattanayak ◽

Harsh K. Verma ◽

Amrit Lal Sangal

Keyword(s):

Community Detection ◽

Ground Truth ◽

Detection Algorithm ◽

Local Information ◽

Ground Truth Data ◽

Overlapping Communities ◽

Detection Algorithms ◽

Np Hard Problem ◽

Community Detection Algorithm ◽

The Individual

Community detection is a pivotal part of network analysis and is classified as an NP-hard problem. In this paper, a novel community detection algorithm is proposed, which probabilistically predicts communities’ diameter using the local information of random seed nodes. The gravitation method is then applied to discover communities surrounding the seed nodes. The individual communities are combined to get the community structure of the whole network. The proposed algorithm, named as Local Gravitational community detection algorithm (LGCDA), can also work with overlapping communities. LGCDA algorithm is evaluated based on quality metrics and ground-truth data by comparing it with some of the widely used community detection algorithms using synthetic and real-world networks.

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An Overlapping Communities Detection Algorithm via Maxing Modularity in Opportunistic Networks

ITM Web of Conferences ◽

10.1051/itmconf/20160705007 ◽

2016 ◽

Vol 7 ◽

pp. 05007

Author(s):

Zhi-Peng Gao ◽

Jing-Jing Zhang ◽

Kun Niu

Keyword(s):

Detection Algorithm ◽

Opportunistic Networks ◽

Overlapping Communities

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DISC: Disambiguating homonyms using graph structural clustering

Journal of Information Science ◽

10.1177/0165551518761011 ◽

2018 ◽

Vol 44 (6) ◽

pp. 830-847 ◽

Cited By ~ 4

Author(s):

Ijaz Hussain ◽

Sohail Asghar

Keyword(s):

Ground Truth ◽

Detection Algorithm ◽

Bibliographic Databases ◽

Data Sets ◽

Database Integration ◽

Data Set ◽

Entity Disambiguation ◽

Structural Clustering ◽

Community Detection Algorithm ◽

Almost All

Author name ambiguity degrades information retrieval, database integration, search results and, more importantly, correct attributions in bibliographic databases. Some unresolved issues include how to ascertain the actual number of authors, how to improve the performance and how to make the method more effective in terms of representative clustering metrics (average cluster purity, average author purity, K-metric, pairwise precision, pairwise recall, pairwise-F1, cluster precision, cluster recall and cluster-F1). It is a non-trivial task to disambiguate authors using only the implicit bibliographic information. An effective method ‘DISC’ is proposed that uses graph community detection algorithm, feature vectors and graph operations to disambiguate homonyms. The citation data set is pre-processed and ambiguous author blocks are formed. A co-authors graph is constructed using authors and their co-author’s relationships. A graph structural clustering ‘gSkeletonClu’ is applied to identify hubs, outliers and clusters of nodes in a co-author’s graph. Homonyms are resolved by splitting these clusters of nodes across the hub if their feature vector similarity is less than a predefined threshold. DISC utilises only co-authors and titles that are available in almost all bibliographic databases. With little modifications, DISC can also be used for entity disambiguation. To validate the DISC performance, experiments are performed on two Arnetminer data sets and compared with five previous unsupervised methods. Despite using limited bibliographic metadata, DISC achieves on average K-metric, pairwise-F1, and cluster-F1 of 92%, 84% and 74%, respectively, using Arnetminer-S and 86%, 80% and 57%, respectively, using Arnetminer-L. About 77.5% and 73.2% clusters are within the range (ground truth clusters ± 3) in Arnetminer-S and Arnetminer-L, respectively.

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A UNIFIED COMMUNITY DETECTION ALGORITHM IN LARGE-SCALE COMPLEX NETWORKS

Advances in Complex Systems ◽

10.1142/s0219525919500048 ◽

2019 ◽

Vol 22 (03) ◽

pp. 1950004

Author(s):

HAO LONG ◽

XIAO-WEI LIU

Keyword(s):

Complex Networks ◽

Community Detection ◽

Large Scale ◽

Detection Algorithm ◽

Component Structure ◽

Overlapping Communities ◽

Expansion Strategy ◽

High Scalability ◽

Community Detection Algorithm ◽

Large Scale Networks

A community is the basic component structure of complex networks and is important for network analysis. In recent decades, researchers from different fields have witnessed a boom of community detection, and many algorithms were proposed to retrieve disjoint or overlapping communities. In this paper, a unified expansion approach is proposed to obtain two different network partitions, which can provide divisions with higher accuracies and have high scalability in large-scale networks. First, we define the edge intensity to quantify the densities of network edges, a higher edge intensity indicates a more compact pair of nodes. Second, vertices of higher density edges are extracted out and denoted as core nodes, whereas other vertices are treated as margin nodes; finally we apply an expansion strategy to form disjoint communities: closely connected core nodes are combined as disjoint skeleton communities, and margin nodes are gradually attached to the nearest skeleton communities. To detect overlapping communities, extra steps are adopted: potential overlapping nodes are identified from the existing disjoint communities and replicated; and communities that bear replicas are further partitioned into smaller clusters. Because replicas of potential overlapping nodes might remain in different communities, overlapping communities can be acquired. Experimental results on real and synthetic networks illustrate higher accuracy and better performance of our method.

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Overlapping Community Detection Based on Node Importance and Adjacency Information

Security and Communication Networks ◽

10.1155/2021/8690662 ◽

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.

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

Modern Physics Letters B ◽

10.1142/s0217984919503226 ◽

2019 ◽

Vol 33 (26) ◽

pp. 1950322 ◽

Cited By ~ 1

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.

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

Intelligent Decision Technologies ◽

10.3233/idt-200059 ◽

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.

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