  In recent years, Graph Neural Networks (GNNs), which can naturally integrate
node information and topological structure, have been demonstrated to be
powerful in learning on graph data. These advantages of GNNs provide great
potential to advance social recommendation since data in social recommender
systems can be represented as user-user social graph and user-item graph; and
learning latent factors of users and items is the key. However, building social
recommender systems based on GNNs faces challenges. For example, the user-item
graph encodes both interactions and their associated opinions; social relations
have heterogeneous strengths; users involve in two graphs (e.g., the user-user
social graph and the user-item graph). To address the three aforementioned
challenges simultaneously, in this paper, we present a novel graph neural
network framework (GraphRec) for social recommendations. In particular, we
provide a principled approach to jointly capture interactions and opinions in
the user-item graph and propose the framework GraphRec, which coherently models
two graphs and heterogeneous strengths. Extensive experiments on two real-world
datasets demonstrate the effectiveness of the proposed framework GraphRec. Our
code is available at \url{https://github.com/wenqifan03/GraphRec-WWW19}
