A Skeleton-based Community Detection Algorithm for Directed Networks

Long, Hao; Wu, Tong; Yin, Hong

doi:10.1109/iciscae51034.2020.9236804

Cited by 3 publications

(1 citation statement)

References 40 publications

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“…As far as we know, MSTs have not been implemented to solve community detection problems in this way before. Even though our method is designed for undirected networks, in the case of the existence of a skeleton network (it can be any tree (cycle-less graph) that preserves the local characteristics of the network), it can be implemented on diferent networks such as temporal networks, directed networks, and multilayer networks [38][39][40].…”

Section: Combinatorial Perspectivementioning

confidence: 99%

Detecting Communities in Complex Networks Using an Adaptive Genetic Algorithm and Node Similarity-Based Encoding

2023

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Detecting communities in complex networks can shed light on the essential characteristics and functions of the modeled phenomena. This topic has attracted researchers from both academia and industry. Among different community detection methods, genetic algorithms (GAs) have become popular. Considering the drawbacks of the currently used locus-based and solution-vector-based encodings to represent the individuals, in this article, we propose (1) a new node similarity-based encoding method, MST-based encoding, to represent a network partition as an individual, which can avoid the shortcomings of the previous encoding schemes. Then, we propose (2) a new adaptive genetic algorithm for the purpose of detecting communities in networks, along with (3) a new initial population generation function to improve the convergence time of the algorithm, and (4) a new sine-based adaptive mutation function which adjusts the mutations according to the improvement in the fitness value of the best individual in the population pool. The proposed method combines the similarity-based and modularity-optimization-based approaches to find communities in complex networks in an evolutionary framework. Besides the fact that the proposed encoding can avoid meaningless mutations or disconnected communities, we show that the new initial population generation function and the new adaptive mutation function can improve the convergence time of the algorithm. Several experiments and comparisons were conducted to verify the effectiveness of the proposed method using modularity and NMI measures on both real-world and synthetic datasets. The results show that the proposed method can find the communities in a significantly shorter time than other GAs while reaching a better trade-off in the different measures.

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Section: Combinatorial Perspectivementioning

confidence: 99%

Detecting Communities in Complex Networks Using an Adaptive Genetic Algorithm and Node Similarity-Based Encoding

2023

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Community Detection Using Label Propagation Algorithm with Random Walk Approach

Patel¹,

Verma

2022

Lecture Notes in Electrical Engineering

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An evaluation tool for backbone extraction techniques in weighted complex networks

Yassin,

Haidar,

Cherifi

et al. 2023

Sci Rep

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Networks are essential for analyzing complex systems. However, their growing size necessitates backbone extraction techniques aimed at reducing their size while retaining critical features. In practice, selecting, implementing, and evaluating the most suitable backbone extraction method may be challenging. This paper introduces , a Python package designed for assessing the performance of backbone extraction techniques in weighted networks. Its comparison framework is the standout feature of . Indeed, the tool incorporates state-of-the-art backbone extraction techniques. Furthermore, it provides a comprehensive suite of evaluation metrics allowing users to evaluate different backbones techniques. We illustrate the flexibility and effectiveness of through the US air transportation network analysis. We compare the performance of different backbone extraction techniques using the evaluation metrics. We also show how users can integrate a new backbone extraction method into the comparison framework. is publicly available as an open-source tool, ensuring its accessibility to researchers and practitioners. Promoting standardized evaluation practices contributes to the advancement of backbone extraction techniques and fosters reproducibility and comparability in research efforts. We anticipate that will serve as a valuable resource for researchers and practitioners enabling them to make informed decisions when selecting backbone extraction techniques to gain insights into the structural and functional properties of complex systems.

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A Skeleton-based Community Detection Algorithm for Directed Networks

Cited by 3 publications

References 40 publications

Detecting Communities in Complex Networks Using an Adaptive Genetic Algorithm and Node Similarity-Based Encoding

Detecting Communities in Complex Networks Using an Adaptive Genetic Algorithm and Node Similarity-Based Encoding

Community Detection Using Label Propagation Algorithm with Random Walk Approach

An evaluation tool for backbone extraction techniques in weighted complex networks

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