  Networks have been a general tool for representing, analyzing, and modeling
relational data arising in several domains. One of the most important aspect of
network analysis is community detection or network clustering. Until recently,
the major focus have been on discovering community structure in single (i.e.,
monoplex) networks. However, with the advent of relational data with multiple
modalities, multiplex networks, i.e., networks composed of multiple layers
representing different aspects of relations, have emerged. Consequently,
community detection in multiplex network, i.e., detecting clusters of nodes
shared by all layers, has become a new challenge. In this paper, we propose
Network Fusion for Composite Community Extraction (NF-CCE), a new class of
algorithms, based on four different non-negative matrix factorization models,
capable of extracting composite communities in multiplex networks. Each
algorithm works in two steps: first, it finds a non-negative, low-dimensional
feature representation of each network layer; then, it fuses the feature
representation of layers into a common non-negative, low-dimensional feature
representation via collective factorization. The composite clusters are
extracted from the common feature representation. We demonstrate the superior
performance of our algorithms over the state-of-the-art methods on various
types of multiplex networks, including biological, social, economic, citation,
phone communication, and brain multiplex networks.
