Relational data arising in many domains can be
represented by networks (or graphs) with nodes capturing
entities and edges representing relationships between these entities.
Community detection in networks has become one of the
most important problems having a broad range of applications.
Until recently, the vast majority of papers have focused on
discovering community structures in a single network. However,
with the emergence of multi-view network data in many realworld
applications and consequently with the advent of multilayer
graph representation, community detection in multi-layer
graphs has become a new challenge. Multi-layer graphs provide
complementary views of connectivity patterns of the same set of
vertices. Fusion of the network layers is expected to achieve better
clustering performance. In this paper, we propose two novel
methods, coined as WSSNMTF (Weighted Simultaneous Symmetric
Non-Negative Matrix Tri-Factorization) and NG-WSSNMTF
(Natural Gradient WSSNMTF), for fusion and clustering of
multi-layer graphs. Both methods are robust with respect to
missing edges and noise. We compare the performance of the
proposed methods with two baseline methods, as well as with
three state-of-the-art methods on synthetic and three real-world
datasets. The experimental results indicate superior performance
of the proposed methods.