  Network clustering tackles the problem of identifying sets of nodes
(communities) that have similar connection patterns. However, in many
scenarios, nodes also have attributes that are correlated with the clustering
structure. Thus, network information (edges) and node information (attributes)
can be jointly leveraged to design high-performance clustering algorithms.
Under a general model for the network and node attributes, this work
establishes an information-theoretic criterion for the exact recovery of
community labels and characterizes a phase transition determined by the
Chernoff-Hellinger divergence of the model. The criterion shows how network and
attribute information can be exchanged in order to have exact recovery (e.g.,
more reliable network information requires less reliable attribute
information). This work also presents an iterative clustering algorithm that
maximizes the joint likelihood, assuming that the probability distribution of
network interactions and node attributes belong to exponential families. This
covers a broad range of possible interactions (e.g., edges with weights) and
attributes (e.g., non-Gaussian models), as well as sparse networks, while also
exploring the connection between exponential families and Bregman divergences.
Extensive numerical experiments using synthetic data indicate that the proposed
algorithm outperforms classic algorithms that leverage only network or only
attribute information as well as state-of-the-art algorithms that also leverage
both sources of information. The contributions of this work provide insights
into the fundamental limits and practical techniques for inferring community
labels on node-attributed networks.
