IEDC: An integrated approach for overlapping and non-overlapping community detection

Hajiabadi, Mahdi; Zare, Hadi; Bobarshad, Hossein

doi:10.1016/j.knosys.2017.02.018

Cited by 43 publications

(11 citation statements)

References 60 publications

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“…The first experiment is to test our algorithm on synthetic networks, and the second experiment is to test our algorithm on real-world networks. Furthermore, we also applied the same networks in the competing algorithms, including NLA [33], MCMOEA [36], COPRA [37], CPM [14], BASH [38], IEDC [39], ACC [40] and Li et al [13]. These competing algorithms listed in TABLE I.…”

Section: Methodsmentioning

confidence: 99%

A Community Merger of Optimization Algorithm to Extract Overlapping Communities in Networks

Jiang

Wang

et al. 2019

IEEE Access

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A community in networks is a subset of vertices primarily connecting internal components, yet less connecting to the external vertices. The existing algorithms aim to extract communities of the topological features in networks. However, the edges of practical complex networks involving a weight that represents the tightness degree of connection and robustness, which leads a significant influence on the accuracy of community detection. In our study, we propose an overlapping community detection method based on the seed expansion strategy applying to both the unweighted and the weighted networks, called OCSE. First, it redefines the edge weight and the vertex weight depending on the influence of the network topology and the original edge weight, and then selects the seed vertices and updates the edges weight. Comparisons between OCSE approach and existing community detection methods on synthetic and real-world networks, the results of the experiment show that our proposed approach has the significantly better performance in terms of the accuracy. INDEX TERMS Overlapping community detection, complex network, weighted network, dense subgraph.

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Section: Methodsmentioning

confidence: 99%

A Community Merger of Optimization Algorithm to Extract Overlapping Communities in Networks

Jiang

Wang

et al. 2019

IEEE Access

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“…W which captures the correlation level between the communities and the generative features, is updated by FindDerivationW and UpdateW according to equations (15) and (16). β is updated based on equations (17) and (18). The update procedure is repeated until the convergence criterion is met.…”

Section: Pfcd Algorithmmentioning

confidence: 99%

Detection of Community Structures in Networks With Nodal Features based on Generative Probabilistic Approach

Zare

Hajiabadi

Jalili

2021

IEEE Trans. Knowl. Data Eng.

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Community detection is considered as a fundamental task in analyzing social networks. Even though many techniques have been proposed for community detection, most of them are based exclusively on the connectivity structures. However, there are node features in real networks, such as gender types in social networks, feeding behavior in ecological networks, and location on e-trading networks, that can be further leveraged with the network structure to attain more accurate community detection methods. We propose a novel probabilistic graphical model to detect communities by taking into account both network structure and nodes' features. The proposed approach learns the relevant features of communities through a generative probabilistic model without any prior assumption on the communities. Furthermore, the model is capable of determining the strength of node features and structural elements of the networks on shaping the communities. The effectiveness of the proposed approach over the state-of-the-art algorithms is revealed on synthetic and benchmark networks.

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“…We compare EnCoD with the following seven non-ensemble based overlapping CD algorithms that cover different types of overlapping CD heuristics: OSLOM [24], EAGLE [48], COPRA [49], SLPA [50], MOSES [51], BIG-CLAM [8] and IEDC [31]. We further compare EnCoD with PVOC [9] and MEDOC [1] which are the most recent algorithms that use disjoint community structure to detect the overlapping communities.…”

Section: Baseline Algorithmsmentioning

confidence: 99%

Ensemble-based overlapping community detection using disjoint community structures

Chakraborty

Ghosh

Park

2019

Knowledge-Based Systems

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While there has been a plethora of approaches for detecting disjoint communities from real-world complex networks, some methods for detecting overlapping community structures have also been recently proposed. In this work, we argue that, instead of developing separate approaches for detecting overlapping communities, a promising alternative is to infer the overlapping communities from multiple disjoint community structures. We propose an ensemble-based approach, called EnCoD, that leverages the solutions produced by various disjoint community detection algorithms to discover the overlapping community structure. Specifically, EnCoD generates a feature vector for each vertex from the results of the base algorithms and learns which features lead to detect densely connected overlapping regions in an unsupervised way. It keeps on iterating until the likelihood of each vertex belonging to its own community maximizes. Experiments on both synthetic and several realworld networks (with known ground-truth community structures) reveal that EnCoD significantly outperforms nine state-of-the-art overlapping community detection algorithms. Finally, we show that EnCoD is generic enough to be applied to networks where the vertices are associated with explicit semantic features. To the best of our knowledge, EnCoD is the second ensemble-based overlapping community detection approach after MEDOC [1].

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IEDC: An integrated approach for overlapping and non-overlapping community detection

Cited by 43 publications

References 60 publications

A Community Merger of Optimization Algorithm to Extract Overlapping Communities in Networks

A Community Merger of Optimization Algorithm to Extract Overlapping Communities in Networks

Detection of Community Structures in Networks With Nodal Features based on Generative Probabilistic Approach

Ensemble-based overlapping community detection using disjoint community structures

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