Closeness Centrality Detection in Homogeneous Multilayer Networks

Hamza Reza Pavel, Anamitra Roy, Abhishek Santra, Sharma Chakravarthy

2023

Abstract

Centrality measures for simple graphs are well-defined and several main-memory algorithms exist for each. Simple graphs have been shown to be not adequate for modeling complex data sets with multiple types of entities and relationships. Although multilayer networks (or MLNs) have been shown to be better suited, there are very few algorithms for centrality measure computation directly on MLNs. Typically, they are converted (aggregated or projected) to simple graphs using Boolean AND or OR operators to compute various centrality measures, which is not only inefficient but incurs a loss of structure and semantics. In this paper, algorithms have been proposed that compute closeness centrality on an MLN directly using a novel decoupling-based approach. Individual results of layers (or simple graphs) of an MLN are used and a composition function is developed to compute the closeness centrality nodes for the MLN. The challenge is to do this efficiently while preserving the accuracy of results with respect to the ground truth. However, since these algorithms use only layer information and do not have complete information of the MLN, computing a global measure such as closeness centrality is a challenge. Hence, these algorithms rely on heuristics derived from intuition. The advantage is that this approach lends itself to parallelism and is more efficient than the traditional approach. Two heuristics, termed CC1 and CC2, have been presented for composition and their accuracy and efficiency have been empirically validated on a large number of synthetic and real-world-like graphs with diverse characteristics. CC1 is prone to generate false negatives whereas CC2 reduces them, is more efficient, and improves accuracy.

Paper Citation

in Harvard Style

Reza Pavel H., Roy A., Santra A. and Chakravarthy S. (2023). Closeness Centrality Detection in Homogeneous Multilayer Networks. In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN 978-989-758-671-2, SciTePress, pages 17-29. DOI: 10.5220/0012159500003598

in Bibtex Style

@conference{kdir23,
author={Hamza Reza Pavel and Anamitra Roy and Abhishek Santra and Sharma Chakravarthy},
title={Closeness Centrality Detection in Homogeneous Multilayer Networks},
booktitle={Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2023},
pages={17-29},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012159500003598},
isbn={978-989-758-671-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - Closeness Centrality Detection in Homogeneous Multilayer Networks
SN - 978-989-758-671-2
AU - Reza Pavel H.
AU - Roy A.
AU - Santra A.
AU - Chakravarthy S.
PY - 2023
SP - 17
EP - 29
DO - 10.5220/0012159500003598
PB - SciTePress