  Complex systems, abstractly represented as networks, are ubiquitous in
everyday life. Analyzing and understanding these systems requires, among
others, tools for community detection. As no single best community detection
algorithm can exist, robustness across a wide variety of problem settings is
desirable. In this work, we present Synwalk, a random walk-based community
detection method. Synwalk builds upon a solid theoretical basis and detects
communities by synthesizing the random walk induced by the given network from a
class of candidate random walks. We thoroughly validate the effectiveness of
our approach on synthetic and empirical networks, respectively, and compare
Synwalk's performance with the performance of Infomap and Walktrap. Our results
indicate that Synwalk performs robustly on networks with varying mixing
parameters and degree distributions. We outperform Infomap on networks with
high mixing parameter, and Infomap and Walktrap on networks with many small
communities and low average degree. Our work has a potential to inspire further
development of community detection via synthesis of random walks and we provide
concrete ideas for future research.
