Soft Overlapping Community Detection in Large-Scale Networks via Fast Fuzzy Modularity Maximization

Yazdanparast, Sakineh; Havens, Timothy C.; Jamalabdollahi, Mohsen

doi:10.1109/tfuzz.2020.2980502

Cited by 21 publications

(11 citation statements)

References 41 publications

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“…is section tests the performance of OCDIF on synthetic networks and real networks. We select the overlapping community detection algorithms based on the global structure and local structure of the static networks as the comparison objects of OCDIF (CLPA [29], GREESE [30], ILPA [31], LMD [32], McFFMM [33], MCMOEA [34], MPEA [35], and SSLPA [36]). We use the following two common indicators to evaluate the quality of the community detection: (1) F1-Score (average F1 value) and ( 2) NMI (normalized mutual information).…”

Section: Community Detection Resultsmentioning

confidence: 99%

A Local Extended Algorithm Combined with Degree and Clustering Coefficient to Optimize Overlapping Community Detection

Liu

Guo

2021

Complexity

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Community structure is one of the most important characteristics of complex networks, which has important applications in sociology, biology, and computer science. The community detection method based on local expansion is one of the most adaptable overlapping community detection algorithms. However, due to the lack of effective seed selection and community optimization methods, the algorithm often gets community results with lower accuracy. In order to solve these problems, we propose a seed selection algorithm of fusion degree and clustering coefficient. The method calculates the weight value corresponding to degree and clustering coefficient by entropy weight method and then calculates the weight factor of nodes as the seed node selection order. Based on the seed selection algorithm, we design a local expansion strategy, which uses the strategy of optimizing adaptive function to expand the community. Finally, community merging and isolated node adjustment strategies are adopted to obtain the final community. Experimental results show that the proposed algorithm can achieve better community partitioning results than other state-of-the-art algorithms.

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Section: Community Detection Resultsmentioning

confidence: 99%

A Local Extended Algorithm Combined with Degree and Clustering Coefficient to Optimize Overlapping Community Detection

Liu

Guo

2021

Complexity

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“…al. [4] proposed a new clustering technique for overlapping clusters using a fuzzy system. They developed Fast Fuzzy Modularity Maximization (FFMM) for finding communities in overlapping networks.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, determining the proper group for the nodes in the network is not an easy task. In this regard, researchers have developed many clustering algorithms and then applied them to various datasets [4][5][6][7][8][9]. Nevertheless, there is no consensus on what the best clustering algorithm means.…”

Section: Introductionmentioning

confidence: 99%

NICASN: Non-negative Matrix Factorization and Independent Component Analysis for Clustering Social Networks

Tadi¹,

Rueda²,

Alhadidi³

2022

Proceedings of the Canadian Conference on Artificial Intelligence

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Discovering clusters in social networks is of fundamental and practical interest. This paper presents a novel clustering strategy for large-scale highly-connected social networks. We propose a new hybrid clustering technique based on non-negative matrix factorization and independent component analysis for finding complex relationships among users of a huge social network. We extract the important features of the network and then perform clustering on independent and important components of the network. Moreover, we introduce a new k-means centroid initialization method by which we achieve higher efficiency. We apply our approach on four well-known social networks: Facebook, Twitter, Academia and Youtube. We experimentally show that our approach achieves much better results in terms of the Silhouette coefficient compared to well-known counterparts such as Hierarchical Louvain, Multiple Local Community detection, and k-means++.

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“…Link strength is defined based on community structure, i.e., edges between vertices of different communities are regarded as weak links and edges between vertices of one community as regarded as strong links. Considering the fact that many networks do not contain groundtruth community information, we employ modularity maximization [32][33][34], which is a commonly used method for detecting community structure, to learn the link strength.…”

Section: Link Strength Learningmentioning

confidence: 99%

Fairness-Aware Predictive Graph Learning in Social Networks

Wang

Febrinanto

et al. 2022

Mathematics

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Predictive graph learning approaches have been bringing significant advantages in many real-life applications, such as social networks, recommender systems, and other social-related downstream tasks. For those applications, learning models should be able to produce a great prediction result to maximize the usability of their application. However, the paradigm of current graph learning methods generally neglects the differences in link strength, leading to discriminative predictive results, resulting in different performance between tasks. Based on that problem, a fairness-aware predictive learning model is needed to balance the link strength differences and not only consider how to formulate it. To address this problem, we first formally define two biases (i.e., Preference and Favoritism) that widely exist in previous representation learning models. Then, we employ modularity maximization to distinguish strong and weak links from the quantitative perspective. Eventually, we propose a novel predictive learning framework entitled ACE that first implements the link strength differentiated learning process and then integrates it with a dual propagation process. The effectiveness and fairness of our proposed ACE have been verified on four real-world social networks. Compared to nine different state-of-the-art methods, ACE and its variants show better performance. The ACE framework can better reconstruct networks, thus also providing a high possibility of resolving misinformation in graph-structured data.

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Soft Overlapping Community Detection in Large-Scale Networks via Fast Fuzzy Modularity Maximization

Cited by 21 publications

References 41 publications

A Local Extended Algorithm Combined with Degree and Clustering Coefficient to Optimize Overlapping Community Detection

A Local Extended Algorithm Combined with Degree and Clustering Coefficient to Optimize Overlapping Community Detection

NICASN: Non-negative Matrix Factorization and Independent Component Analysis for Clustering Social Networks

Fairness-Aware Predictive Graph Learning in Social Networks

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