  Heterogeneous networks not only present a challenge of heterogeneity in the
types of nodes and relations, but also the attributes and content associated
with the nodes. While recent works have looked at representation learning on
homogeneous and heterogeneous networks, there is no work that has collectively
addressed the following challenges: (a) the heterogeneous structural
information of the network consisting of multiple types of nodes and relations;
(b) the unstructured semantic content (e.g., text) associated with nodes; and
(c) online updates due to incoming new nodes in growing network. We address
these challenges by developing a Content-Aware Representation Learning model
(CARL). CARL performs joint optimization of heterogeneous SkipGram and deep
semantic encoding for capturing both heterogeneous structural closeness and
unstructured semantic relations among all nodes, as function of node content,
that exist in the network. Furthermore, an additional online update module is
proposed for efficiently learning representations of incoming nodes. Extensive
experiments demonstrate that CARL outperforms state-of-the-art baselines in
various heterogeneous network mining tasks, such as link prediction, document
retrieval, node recommendation and relevance search. We also demonstrate the
effectiveness of the CARL's online update module through a category
visualization study.
