Empirical analysis of community detection algorithms

Jagadishwari, V.; Umadevi, V.

doi:10.1109/icecct.2017.8117886

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…Community detection methods usually use global criteria to detect all the communities from an entire graph, where the focus is more on quality (e.g., cohesiveness) than efficiency. VOLUME 4, 2016 [7], [19], [20], [28], [40], [41], [44], [45]. Community search is a fundamental problem in graph analysis [2].…”

Section: Introductionmentioning

confidence: 99%

Influential Attributed Communities Search in Large Networks (InfACom)

2021

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Community search is a fundamental problem in graph analysis. In many applications, network nodes have specific properties that are essential for making sense of communities. In these networks, attributes are associated with nodes to capture their properties. The community influence is a key property of the community that can be employed to sort the communities in a network based on the relevance/importance of certain attributes. Unfortunately, most of the previously introduced community search algorithms over attributed networks neglected the community influence. In this paper, we study the influential attributed community search problem. Different factors for measuring the influence are discussed. Also, different Influential Attributed Community (InfACom) algorithms based on the concept of k-clique are proposed. Two techniques are presented one for sequential implementation with three variations and one for parallel implementation. In addition, we propose efficient algorithms for maintaining the proposed algorithms on dynamic graphs. The proposed algorithms are evaluated on different real datasets. The experimental results show that the summarization technique reduces the size of the graph by approximately half. In addition, it shows that the proposed algorithms EnhancedExact and Approximate outperform the state-of-the-art approaches Incremental Time efficient (Inc − T ), Incremental Space efficient (Inc − S), Exact, and 2-Approximation (AppInc) in both efficiency and effectiveness. For the EnhancedExact algorithm, the results show that the efficiency is at least 7 times faster than the Inc − S algorithm, at least 4.5 times faster than the Inc − T algorithm, and 2 times faster than the AppInc algorithm. For the Approximate algorithm, the results show that its efficiency is at least 10 times faster than the Inc − S algorithm, at least 6.4 times faster than the Inc − T algorithm, and 3 times faster than the AppInc algorithm. Finally, the results show that the proposed algorithms retrieve cohesive communities with a smaller diameter than all the state-of-the-art approaches.

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Section: Introductionmentioning

confidence: 99%

Influential Attributed Communities Search in Large Networks (InfACom)

2021

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Author Collaboration in Ten Years of IPS²: A Bibliometric Analysis

2019

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Influential Attributed Communities via Graph Convolutional Network (InfACom-GCN)

2022

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Community search is a basic problem in graph analysis. In many applications, network nodes have certain properties that are important for the community to make sense of the application; hence, attributes are associated with nodes to capture their properties. Community influence is an important community property that can be used to rank communities in a network based on the relevance/importance of a particular attribute. Unfortunately, most of the community search algorithms introduced previously in attributed networks research work ignored the community influence. When searching for influential communities, two potential data sources can be used: network attributes and nodes. Dealing with structure-related attributes is a challenge. Recently, the graph neural network (GNN) has completely changed the field of graph representation learning by effectively learning node embedding and has achieved the most advanced results in tasks such as node classification and connection prediction. In this paper, we investigate the problem of searching for the influential communities in attributed networks. We propose an efficient algorithm for retrieving the influential communities in a large attributed network. The proposed approach contains two main steps: (1) Community detection using a graph convolutional network in a semi-supervised learning setting considering the correlation between attributes and the overall graph information, and (2) constructing the influential communities resulting from step 1. The proposed approach is evaluated on various real datasets. The experimental results show the efficiency and effectiveness of the proposed implementations.

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Empirical analysis of community detection algorithms

Cited by 3 publications

References 8 publications

Influential Attributed Communities Search in Large Networks (InfACom)

Influential Attributed Communities Search in Large Networks (InfACom)

Author Collaboration in Ten Years of IPS²: A Bibliometric Analysis

Influential Attributed Communities via Graph Convolutional Network (InfACom-GCN)

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