Archaeological networks, community detection, and critical scales of interaction in the U.S. Southwest/Mexican Northwest

Peeples, Matthew A.; Bischoff, Robert

doi:10.1016/j.jaa.2023.101511

Cited by 3 publications

(1 citation statement)

References 52 publications

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“…Each of the past research initiatives analysed the network's content from a unique perspective and then used this analysis to discover communities by considering the topological information of a network (Ullah et al, 2022). Recent trends in the practical application of community detection in network structure especially using the Louvain and Leiden algorithm, are in healthcare (Chatterjee & Sanjeev, 2023;Evans et al, 2022;Jin et al, 2020;Kabir et al, 2019;Kramer et al, 2020;Nallusamy & Easwarakumar, 2023;Nicolini et al, 2017;Rahiminejad et al, 2019), social network (Chessa et al, 2023;Irsyad & Rakhmawati, 2019;Li et al, 2023;Park & Kwon, 2022;Torene et al, 2022), telecommunication (Ding et al, 2022;Zu et al, 2021), economic (Han et al, 2018;, intelligent (Karyotis et al, 2018;Singhal et al, 2020) and nature (Peeples & Bischoff, 2023;Xie et al, 2022).…”

Section: Hierarchical Clusteringmentioning

confidence: 99%

Identifying Communities with Modularity Metric Using Louvain and Leiden Algorithms

Hairol Anuar,

Abal Abas,

Md Yunos

et al. 2024

JST

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Over the past 20 years, there has been a significant increase in publication in complex network analysis research, especially in community detection. Many methods were proposed to identify community structure. Each community identification algorithm has strengths and weaknesses due to the complexity of information. Among them, the optimisation methods are widely focused on. This paper focuses on an empirical study of two community detection algorithms based on agglomerative techniques using modularity metric: Louvain and Leiden. In this regard, the Louvain algorithm has been shown to produce a bad connection in the community and disconnected when executed iteratively. Therefore, the Leiden algorithm is designed to successively resolve the weaknesses. Performance comparisons between the two and their concept were summarised in detail, as well as the step-by-step learning process of the state-of-the-art algorithms. This study is important and beneficial to the future study of interdisciplinary data sciences of network analysis. First, it demonstrates that the Leiden method outperformed the Louvain algorithm in terms of modularity metric and running time. Second, the paper displays the use of these two algorithms on synthetic and real networks. The experiment was successful as it identified better performance, and future work is required to confirm and validate these findings.

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