Detecting Overlapping Communities in Modularity Optimization by Reweighting Vertices

Tsung, Chen-Kun; Ho, Hann‐Jang; Chen, Chien‐Yu; Chang, Tien-Wei; Lee, SingLing

doi:10.3390/e22080819

Cited by 5 publications

(3 citation statements)

References 38 publications

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“…5) Fuzzy modularity maximization (e.g., GAFCD [16], FMM/H2 [11], GA NWA IMR [35], FFMM [36], SOSFCD [18]) models FCD as a global optimization problem based on a specific fuzzy modularity, and searches for the max-modularity fuzzy partition. Such methods usually have the advantages of high modularity quality, few parameters and strong portability.…”

Section: Problem Formulation Of Fuzzy Community Detectionmentioning

confidence: 99%

Community detection from fuzzy and higher-order perspectives

Xiao,

2023

EPL

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Our ability to observe the mesoscale topology of complex networks through community detection has significantly advanced in the past decades. This progress has opened up new frontiers in discovering more sophisticated and meaningful community structures that possess fuzzy and higher-order characteristics. This review provides an overview of two emerging research directions, which are fuzzy and higher-order community detection. It includes related concepts and practical scenarios, mathematical descriptions and latest advancements, as well as current challenges and future directions. Therefore, it will facilitate researchers in swiftly grasping the two emerging fields, offering valuable insights for future development of community detection studies.

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Section: Problem Formulation Of Fuzzy Community Detectionmentioning

confidence: 99%

Community detection from fuzzy and higher-order perspectives

Xiao,

2023

EPL

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“…Li Y et al [36] proposed the Two Expansions of Seeds (TES) algorithm, which effectively discovers overlapping communities in complex networks, outperforming existing algorithms in terms of robustness, accuracy, and redundancy reduction. Tsung et al [37] proposed a novel algorithm for detecting overlapping communities by introducing a node weight allocation problem and extending the modularity measure, showing improved results in detecting valuable overlapping nodes.…”

Section: Overlapping Community Detectionmentioning

confidence: 99%

GLOD: The Local Greedy Expansion Method for Overlapping Community Detection in Dynamic Provenance Networks

et al. 2023

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Local overlapping community detection is a hot problem in the field of studying complex networks. It is the process of finding dense clusters based on local network information. This paper proposes a method called local greedy extended dynamic overlapping community detection (GLOD) to address the challenges of detecting high-quality overlapping communities in complex networks. The goal is to improve the accuracy of community detection by considering the dynamic nature of community boundaries and leveraging local network information. The GLOD method consists of several steps. First, a coupling seed is constructed by selecting nodes from blank communities (i.e., nodes not assigned to any community) and their similar neighboring nodes. This seed serves as the starting point for community detection. Next, the seed boundaries are extended by applying multiple community fitness functions. These fitness functions determine the likelihood of nodes belonging to a specific community based on various local network properties. By iteratively expanding the seed boundaries, communities with higher density and better internal structure are formed. Finally, the overlapping communities are merged using an improved version of the Jaccard coefficient, which is a measure of similarity between sets. This step ensures that overlapping nodes between communities are properly identified and accounted for in the final community structure. The proposed method is evaluated using real networks and three sets of LFR (Lancichinetti–Fortunato–Radicchi) networks, which are synthetic benchmark networks widely used in community detection research. The experimental results demonstrate that GLOD outperforms existing algorithms and achieves a 2.1% improvement in the F-score, a community quality evaluation metric, compared to the LOCD framework. It outperforms the best existing LOCD algorithm on the real provenance network. In summary, the GLOD method aims to overcome the limitations of existing community detection algorithms by incorporating local network information, considering overlapping communities, and dynamically adjusting community boundaries. The experimental results suggest that GLOD is effective in improving the quality of community detection in complex networks.

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“…In the paper “Detecting Overlapping Communities in Modularity Optimization by Reweighting Vertices” by Tsung et al [ 5 ], the community detection problem was considered, specifically focusing on overlapping community sets of nodes. By first introducing a node weight allocation problem to formulate the overlapping property, the authors proposed a genetic algorithm, exploiting an extension of the modularity function for solving the node weight allocation problem and detecting the overlapping communities.…”

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confidence: 99%