Spectral clustering methods for multiplex networks

Daryl R. DeFord and Scott D. Pauls

Physica A: Statistical Mechanics and its Applications, 2019, vol. 533, issue C

Abstract: Clustering nodes of a network into communities is a basic tool for identifying structure in complex system. We extend spectral clustering to multiplex structures and discuss some of the difficulties that arise in defining natural generalizations of the single layer case. To analyze the effectiveness of our approaches, we construct three simple families of synthetic multiplex networks and compare the performance of two different versions of multiplex spectral clustering. Our two clustering models – one based on the diagonal supra-Laplacian and the other based on a different diffusive model – perform differently on our tests. The first essentially interpolates between splitting the layers of the networks into different clusters and finding the layer clusters while the second interpolates between the layer clusterings, often finding clusters that span the different layers which reflect multiplex characteristics of the network.

Keywords: Multiplex networks; Spectral clustering (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:533:y:2019:i:c:s0378437119311379

DOI: 10.1016/j.physa.2019.121949

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