Title: Neural-Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding,
Abstract: Network embedding methodologies, which learn a distributed vector
representation for each vertex in a network, have attracted considerable
interest in recent years. Existing works have demonstrated that vertex
representation learned through an embedding method provides superior
performance in many real-world applications, such as node classification, link
prediction, and community detection. However, most of the existing methods for
network embedding only utilize topological information of a vertex, ignoring a
rich set of nodal attributes (such as, user profiles of an online social
network, or textual contents of a citation network), which is abundant in all
real-life networks. A joint network embedding that takes into account both
attributional and relational information entails a complete network information
and could further enrich the learned vector representations. In this work, we
present Neural-Brane, a novel Neural Bayesian Personalized Ranking based
Attributed Network Embedding. For a given network, Neural-Brane extracts latent
feature representation of its vertices using a designed neural network model
that unifies network topological information and nodal attributes; Besides, it
utilizes Bayesian personalized ranking objective, which exploits the proximity
ordering between a similar node-pair and a dissimilar node-pair. We evaluate
the quality of vertex embedding produced by Neural-Brane by solving the node
classification and clustering tasks on four real-world datasets. Experimental
results demonstrate the superiority of our proposed method over the
state-of-the-art existing methods.