Degree Centrality Algorithms For Homogeneous Multilayer Networks

TL;DR

This paper introduces heuristic algorithms for directly computing degree centrality in multilayer networks, preserving structure and semantics, and demonstrating improved efficiency over traditional aggregation methods.

Contribution

It proposes a novel decoupling-based approach for calculating degree centrality in MLNs, avoiding information loss and enabling parallel processing.

Findings

Decoupling approach is more efficient than aggregation methods.

Algorithms preserve network structure and semantics.

Experimental results show high accuracy and efficiency.

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) the 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 (HoMLN) 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 characteristics to validate the accuracy and efficiency of our proposed algorithms.