Abstract
Community discovery is one of the most challenging tasks in social network analysis. During the last decades, several algorithms have been proposed with the aim of identifying communities in complex networks, each one searching for mesoscale topologies having different and peculiar characteristics. Among such vast literature, an interesting family of Community Discovery algorithms, designed for the analysis of social network data, is represented by overlapping, node-centric approaches. In this work, following such line of research, we propose Angel, an algorithm that aims to lower the computational complexity of previous solutions while ensuring the identification of high-quality overlapping partitions. We compare Angel, both on synthetic and real-world datasets, against state of the art community discovery algorithms designed for the same community definition. Our experiments underline the effectiveness and efficiency of the proposed methodology, confirmed by its ability to constantly outperform the identified competitors.
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Notes
- 1.
Code available at: https://github.com/GiulioRossetti/ANGEL.
- 2.
All the algorithms were executed on a Linux 4.4.0 machine with an Intel Core i7-5820 CPU @3.3 GHzx16 and 32 GB of RAM.
- 3.
All algorithms have been integrated within the CDlib python library [15]
- 4.
Code available at https://sites.google.com/site/santofortunato/inthepress2.
- 5.
Datasets available at https://snap.stanford.edu/data/.
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Acknowledgment
This work is partially supported by the European Community’s H2020 Program under the funding scheme “INFRAIA-1-2014-2015: Research Infrastructures” grant agreement 654024, http://www.sobigdata.eu, “SoBigData”.
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Rossetti, G. (2020). Exorcising the Demon: Angel, Efficient Node-Centric Community Discovery. In: Cherifi, H., Gaito, S., Mendes, J., Moro, E., Rocha, L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_13
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