Title: Spectral Clustering Methods for Multiplex Networks,
Abstract: Multiplex networks offer an important tool for the study of complex systems
and extending techniques originally designed for single--layer networks is an
important area of study. One of the most important methods for analyzing
networks is clustering the nodes into communities that represent common
connectivity patterns. In this paper we extend spectral clustering to multiplex
structures and discuss some of the difficulties that arise in attempting to
define a natural generalization. In order to analyze our approach, we describe
three simple, synthetic multiplex networks and compare the performance of
different multiplex models. Our results suggest that a dynamically motivated
model is more successful than a structurally motivated model in discovering the
appropriate communities.