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Authors: Clara Pizzuti and Annalisa Socievole

Affiliation: National Research Council of Italy (CNR), Institute for High Performance Computing and Networking (ICAR), Via Pietro Bucci, 8-9C, 87036 Rende (CS), Italy

Keyword(s): Community Detection, Genetic Algorithm, Effective Resistance, Moore-Penrose Pseudoinverse.

Abstract: This work presents a new approach based on genetic algorithms (GAs) and the concept of effective resistance for detecting communities within an undirected graph. The method considers the equivalent electric network of the input graph, where edges are weighted with their effective resistance, a measure of electrical resistance between nodes, whose square root has been shown to be a Euclidean metric. The algorithm computes the similarity between nodes by using the effective resistance values and generates a weighted and sparse graph by adopting a thresholding sparsification strategy based on the nearest neighbors of each node. Experiments over synthetic and real-world networks demonstrate the effectiveness of our approach when compared to other benchmark methods.

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Paper citation in several formats:
Pizzuti, C. and Socievole, A. (2021). An Effective Resistance based Genetic Algorithm for Community Detection. In Proceedings of the 13th International Joint Conference on Computational Intelligence (IJCCI 2021) - IJCCI; ISBN 978-989-758-534-0; ISSN 2184-3236, SciTePress, pages 28-36. DOI: 10.5220/0010644300003063

@conference{ijcci21,
author={Clara Pizzuti and Annalisa Socievole},
title={An Effective Resistance based Genetic Algorithm for Community Detection},
booktitle={Proceedings of the 13th International Joint Conference on Computational Intelligence (IJCCI 2021) - IJCCI},
year={2021},
pages={28-36},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010644300003063},
isbn={978-989-758-534-0},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computational Intelligence (IJCCI 2021) - IJCCI
TI - An Effective Resistance based Genetic Algorithm for Community Detection
SN - 978-989-758-534-0
IS - 2184-3236
AU - Pizzuti, C.
AU - Socievole, A.
PY - 2021
SP - 28
EP - 36
DO - 10.5220/0010644300003063
PB - SciTePress

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