  Heterogeneous information network (HIN) has been widely used to characterize
entities of various types and their complex relations. Recent attempts either
rely on explicit path reachability to leverage path-based semantic relatedness
or graph neighborhood to learn heterogeneous network representations before
predictions. These weakly coupled manners overlook the rich interactions among
neighbor nodes, which introduces an early summarization issue. In this paper,
we propose GraphHINGE (Heterogeneous INteract and aggreGatE), which captures
and aggregates the interactive patterns between each pair of nodes through
their structured neighborhoods. Specifically, we first introduce
Neighborhood-based Interaction (NI) module to model the interactive patterns
under the same metapaths, and then extend it to Cross Neighborhood-based
Interaction (CNI) module to deal with different metapaths. Next, in order to
address the complexity issue on large-scale networks, we formulate the
interaction modules via a convolutional framework and learn the parameters
efficiently with fast Fourier transform. Furthermore, we design a novel
neighborhood-based selection (NS) mechanism, a sampling strategy, to filter
high-order neighborhood information based on their low-order performance. The
extensive experiments on six different types of heterogeneous graphs
demonstrate the performance gains by comparing with state-of-the-arts in both
click-through rate prediction and top-N recommendation tasks.
