Estimating the Number of Communities in Weighted Networks

Qing, Huan

doi:10.3390/e25040551

Cited by 2 publications

(1 citation statement)

References 64 publications

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“…Huang [21] introduced Community Contrastive Learning (Community-CL), an online framework that combines contrastive learning with graph views to simultaneously learn node representations and detect communities in networks, outperforming state-of-the-art baselines in community detection tasks. Qing's [22] approach combines weighted modularity with spectral clustering to accurately estimate the number of communities in weighted networks, including those with negative edge weights and signed networks. Yao [23] introduced a new modularity function F2 and proposed a constrained Louvain algorithm that outperforms other methods, including the classical Louvain algorithm and Newman's fast method, in community detection on various networks.…”

Section: Local 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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Section: Local 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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Bi-objective model for community detection in weighted complex networks

Torres-Cockrell,

Mora-Gutiérrez,

Rincón-García

et al. 2024

EAI Endorsed Trans Ind Net Intel Syst

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In this study, we introduce an innovative approach that utilizes complex networks and the k_core method to address community detection in weighted networks. Our proposed bi-objective model aims to simultaneously discover non-overlapping communities while ensuring that the degree of similarity remains below a critical threshold to prevent network degradation. We leverage the k_core structure to detect tightly interconnected node groups, a concept particularly valuable in edge-weighted networks where different edge weights indicate the strength or importance of node relationships. Beyond maximizing the count of k_core communities, our model seeks a homogeneous weight distribution across edges within these communities, promoting stronger cohesion. To tackle this challenge, we implement two multi-target algorithms: Non-dominated Sorting Genetic Algorithm II (NSGAII) and a Multi-Objective Simulated Annealing (MOSA) algorithm. Both algorithms efficiently identify non-overlapping communities with a specified degree 'k'. The results of our experiments reveal a trade-off between maximizing the number of k_core communities and enhancing the homogeneity of these communities in terms of their minimum weighted interconnections. Notably, the MOSA algorithm outperforms NSGAII in both small and large instances, demonstrating its effectiveness in achieving this balance. This approach sheds light on effective strategies for resolving conflicting goals in community detection within weighted networks.

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Estimating the Number of Communities in Weighted Networks

Cited by 2 publications

References 64 publications

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

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

Bi-objective model for community detection in weighted complex networks

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