2019/09/07 by Abhishek Santra, Santra, Abhishek, Sanjukta Bhowmick +3
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #Bioinformatics and Genomic Networks #Complex Network Analysis Techniques #Computational Drug Discovery Methods #Databases (cs.DB) #FOS: Computer and information sciences #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.1910.01443
openalex publication_date 2019/09/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Networks (or graphs) are used to model the dyadic relations between entities in a complex system. In cases where there exists multiple relations between the entities, the complex system can be represented as a multilayer network, where the network in each layer represents one particular relation (or feature). The analysis of multilayer networks involves combining edges from specific layers and then computing a network property. Different subsets of the layers can be combined. For any Boolean combination operation (e.g. AND, OR), the number of possible subsets is exponential to the number of layers. Thus recomputing for each subset from scratch is an expensive process. In this paper, we propose to efficiently analyze multilayer networks using a method that we term network decomposition. Network decomposition is based on analyzing each network layer individually and then aggregating the analysis results. We demonstrate the effectiveness of using network decomposition for detecting communities on different combinations of network layers. Our results on multilayer networks obtained from real-world and synthetic datasets show that our proposed network decomposition method requires significantly lower computation time while producing results of high accuracy.