  Heterogeneous networks are widely used to model real-world semi-structured
data. The key challenge of learning over such networks is the modeling of node
similarity under both network structures and contents. To deal with network
structures, most existing works assume a given or enumerable set of meta-paths
and then leverage them for the computation of meta-path-based proximities or
network embeddings. However, expert knowledge for given meta-paths is not
always available, and as the length of considered meta-paths increases, the
number of possible paths grows exponentially, which makes the path searching
process very costly. On the other hand, while there are often rich contents
around network nodes, they have hardly been leveraged to further improve
similarity modeling. In this work, to properly model node similarity in
content-rich heterogeneous networks, we propose to automatically discover
useful paths for pairs of nodes under both structural and content information.
To this end, we combine continuous reinforcement learning and deep content
embedding into a novel semi-supervised joint learning framework. Specifically,
the supervised reinforcement learning component explores useful paths between a
small set of example similar pairs of nodes, while the unsupervised deep
embedding component captures node contents and enables inductive learning on
the whole network. The two components are jointly trained in a closed loop to
mutually enhance each other. Extensive experiments on three real-world
heterogeneous networks demonstrate the supreme advantages of our algorithm.
