PercoMCV: A hybrid approach of community detection in social networks

Kasoro, Nathanaël; Kasereka, Selain; Mayogha, Elie; Vinh, Ho Tuong; Kinganga, Joël

doi:10.1016/j.procs.2019.04.010

Cited by 9 publications

(4 citation statements)

References 11 publications

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“…PercoMVC . The PercoMVC approach consists of the two steps [ 31 ]. In the first step, the algorithm attempts to determine all communities that the clique percolation algorithm may find.…”

Section: Methodsmentioning

confidence: 99%

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

Ponomarenko¹,

Pitsoulis

Shamshetdinov³

2021

PLoS ONE

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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.

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“…PercoMVC . The PercoMVC approach consists of the two steps [ 31 ]. In the first step, the algorithm attempts to determine all communities that the clique percolation algorithm may find.…”

Section: Methodsmentioning

confidence: 99%

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

Ponomarenko¹,

Pitsoulis

Shamshetdinov³

2021

PLoS ONE

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“…In traditional community discovery algorithms, the network is typically divided into several mutually exclusive communities, with each node belonging to only one community. Such algorithms [31][32][33][34][35][36][37][38][39] are mainly studied in terms of graph partitioning, clustering, label propagation, etc. However, it is well-documented that nodes may participate in more than one community in some real-world applications [40], meaning that nodes can simultaneously belong to several different clusters.…”

Section: Related Workmentioning

confidence: 99%

CILPA: An Overlapping Community Detection Algorithm Based on Label Propagation and Node Information

Liu

Shen

2023

Preprint

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With the proliferation of complex networks, a large number of effective community detection methods have been developed. Of these, the label propagation-based approach for community detection has proven to be very suitable for community detection. However, it should be noted that this approach is stochastic and unstable. To address these issues, a CILPA method is proposed based on label Propagation and node information to identify a more rational community. First, we obtain the local influence reflecting the local spatial location of nodes based on their neighbourhood relationships. The importance of the nodes is subsequently obtained by combining topological information and local influence. Secondly, a label propagation strategy with preference selection is introduced to obtain stable overlapping communities based on neighbourhood information. More importantly, a label checking strategy is articulated to reduce the impact of sparse connections. Extensive experiments on the LFR benchmark network and real-world networks demonstrate that the approach is both stable and effective.

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“…Clique guided community detection and graph partition methods are the algorithms which use graph concepts for clustering the nodes [16]. Clique percolation technique is merged with a centrality measure [17] to find the community structure from the given graph. But again here major drawback is that some nodes remain unclassified.…”

Section: Related Workmentioning

confidence: 99%

CGraM: Enhanced Algorithm for Community Detection in Social Networks

Nallusamy¹,

Easwarakumar²

2022

Intelligent Automation &Amp; Soft Computing

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Community Detection is used to discover a non-trivial organization of the network and to extract the special relations among the nodes which can help in understanding the structure and the function of the networks. However, community detection in social networks is a vast and challenging task, in terms of detected communities accuracy and computational overheads. In this paper, we propose a new algorithm Enhanced Algorithm for Community Detection in Social Networks -CGraM, for community detection using the graph measures eccentricity, harmonic centrality and modularity. First, the centre nodes are identified by using the eccentricity and harmonic centrality, next a preliminary community structure is formed by finding the similar nodes using the jaccard coefficient. Later communities are selected from the preliminary community structure based on the number of inter-community and intra-community edges between them. Then the selected communities are merged till the modularity improves to form the better resultant community structure. This method is tested on the real networks and the results are evaluated using the evaluation metrics modularity and Normalized Mutual Information (NMI). The results are visualized and also compared with the state-of-the-art algorithms that covers louvian, walktrap, infomap, label propagation, fast greedy and eigen vector for more accurate analysis. CGraM achieved the better modularity and improved NMI values comparatively with other algorithms and gives improved results collaboratively when compared to previous methods.

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PercoMCV: A hybrid approach of community detection in social networks

Cited by 9 publications

References 11 publications

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

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

CILPA: An Overlapping Community Detection Algorithm Based on Label Propagation and Node Information

CGraM: Enhanced Algorithm for Community Detection in Social Networks

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