Degree Centrality Algorithms for Homogeneous Multilayer Networks

Hamza Reza Pavel, Abhishek Santra, Sharma Chakravarthy

2022

Abstract

Centrality measures for simple graphs/networks are well-defined and each has numerous main-memory algorithms. However, for modeling complex data sets with multiple types of entities and relationships, simple graphs are not ideal. MultiLayer Networks (or MLNs) have been proposed for modeling them and have been shown to be better suited in many ways. Since there are no algorithms for computing centrality measures directly on MLNs, existing strategies reduce (aggregate or collapse) MLN layers to simple networks using Boolean AND or OR operators. This approach negates the benefits of MLN modeling as these computations tend to be expensive and furthermore results in loss of structure and semantics. In this paper, we propose heuristic-based algorithms for computing centrality measures (specifically, degree centrality) on MLNs directly (i.e., without reducing them to simple graphs) using a newly-proposed decoupling-based approach which is efficient as well as structure and semantics preserving. We propose multiple heuristics to calculate the degree centrality using the network decoupling-based approach and compare accuracy and precision with Boolean OR aggregated Homogeneous MLNs (HoMLNs) for ground truth. The network decoupling approach can take advantage of parallelism and is more efficient compared to aggregation-based approaches. Extensive experimental analysis is performed on large synthetic and real-world data sets of varying graph characteristics to validate the accuracy, precision, and efficiency of our proposed algorithms.

Paper Citation

in Harvard Style

Pavel H., Santra A. and Chakravarthy S. (2022). Degree Centrality Algorithms for Homogeneous Multilayer Networks. In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR; ISBN 978-989-758-614-9, SciTePress, pages 51-62. DOI: 10.5220/0011528900003335

in Bibtex Style

@conference{kdir22,
author={Hamza Reza Pavel and Abhishek Santra and Sharma Chakravarthy},
title={Degree Centrality Algorithms for Homogeneous Multilayer Networks},
booktitle={Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR},
year={2022},
pages={51-62},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011528900003335},
isbn={978-989-758-614-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR
TI - Degree Centrality Algorithms for Homogeneous Multilayer Networks
SN - 978-989-758-614-9
AU - Pavel H.
AU - Santra A.
AU - Chakravarthy S.
PY - 2022
SP - 51
EP - 62
DO - 10.5220/0011528900003335
PB - SciTePress