Abstract
Most popular algorithms for community detection in graphs have one serious drawback, namely, they are heuristic-based and in many cases are unable to find a near-optimal solution. Moreover, their results tend to exhibit significant volatility. These issues might be solved by a proper initialization of such algorithms with some carefully chosen partition of nodes. In this paper, we investigate the impact of such initialization applied to the two most commonly used community detection algorithms: Louvain and Leiden. We use a partition obtained by embedding the nodes of the graph into some high dimensional space of real numbers and then running a clustering algorithm on this latent representation. We show that this procedure significantly improves the results. Proper embedding filters unnecessary information while retaining the proximity of nodes belonging to the same community. As a result, clustering algorithms ran on these embeddings merge nodes only when they are similar with a high degree of certainty, resulting in a stable and effective initial partition.
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Notes
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EC stands for Embedding–Clustering and denotes the proposed extension of Louvain and Leiden algorithms. If not otherwise stated, the results for the EC algorithm uses the best possible initial partitioning C.
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For details please refer to: https://github.com/bartoszpankratz/ECCD/blob/main/Embedding-Clustering_Community_Detection_Experiment.ipynb.
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Acknowledgements
Hardware used for the computations was provided by the SOSCIP consortium. Launched in 2012, the SOSCIP consortium is a collaboration between Ontario’s research-intensive post-secondary institutions and small- and medium-sized enterprises (SMEs) across the province. Working together with the partners, SOSCIP is driving the uptake of AI and data science solutions and enabling the development of a knowledge-based and innovative economy in Ontario by supporting technical skill development and delivering high-quality outcomes. SOSCIP supports industrial-academic collaborative research projects through partnership-building services and access to leading-edge advanced computing platforms, fuelling innovation across every sector of Ontario’s economy.
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Pankratz, B., Kamiński, B., Prałat, P. (2023). Community Detection Supported by Node Embeddings (Searching for a Suitable Method). In: Cherifi, H., Mantegna, R.N., Rocha, L.M., Cherifi, C., Micciche, S. (eds) Complex Networks and Their Applications XI. COMPLEX NETWORKS 2016 2022. Studies in Computational Intelligence, vol 1078. Springer, Cham. https://doi.org/10.1007/978-3-031-21131-7_17
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