  Different kinds of random walks have proven to be useful in the study of
structural properties of complex networks. Among them, the restricted dynamics
of self-avoiding random walks (SAW), which visit only at most once each vertex
in the same walk, has been successfully used in network exploration. The
detection of communities of strongly connected vertices in networks remains an
open problem, despite its importance, due to the high computational complexity
of the associated optimization problem and the lack of a unique formal
definition of communities. In this work, we propose a SAW-based method to
extract the community distribution of a network and show that it achieves high
modularity scores, specially for real-world networks. We combine SAW with
principal component analysis to define the dissimilarity measure to be used for
agglomerative hierarchical clustering. To evaluate the performance of this
method we compare it with four popular methods for community detection:
Girvan-Newman, Fastgreedy, Walktrap and Infomap using two types of synthetic
networks and six well-known real-world cases.
