  Automatically learning thematic clusters in network data has long been a
challenging task in machine learning community. A number of approaches have
been proposed to accomplish it, utilizing edges, vertex features, or both
aforementioned. However, few of them consider how the quantification of
dichotomous inclination w.r.t. network topology and vertex features may
influence vertex-cluster preferences, which deters previous methods from
uncovering more interpretable latent groups in network data. To fill this void,
we propose a novel probabilistic model, dubbed Relational Thematic Clustering
with Mutually Preferred Neighbors (RTCMPN). Different from prevalent approaches
which predetermine the learning significance of edge structure and vertex
features, RTCMPN can further learn the latent preferences indicating which
neighboring vertices are more possible to be in the same cluster, and the
dichotomous inclinations describing how relative significance w.r.t. edge
structure and vertex features may impact the association between pairwise
vertices. Therefore, cluster structure implanted with edge structure, vertex
features, neighboring preferences, and vertex-vertex dichotomous inclinations
can be learned by RTCMPN. We additionally derive an effective
Expectation-Maximization algorithm for RTCMPN to infer the optimal model
parameters. RTCMPN has been compared with several strong baselines on various
network data. The remarkable results validate the effectiveness of RTCMPN.
