  Graph Neural Networks (GNNs) have been successfully adopted in recommender
systems by virtue of the message-passing that implicitly captures collaborative
effect. Nevertheless, most of the existing message-passing mechanisms for
recommendation are directly inherited from GNNs without scrutinizing whether
the captured collaborative effect would benefit the prediction of user
preferences. In this paper, we first analyze how message-passing captures the
collaborative effect and propose a recommendation-oriented topological metric,
Common Interacted Ratio (CIR), which measures the level of interaction between
a specific neighbor of a node with the rest of its neighbors. After
demonstrating the benefits of leveraging collaborations from neighbors with
higher CIR, we propose a recommendation-tailored GNN, Collaboration-Aware Graph
Convolutional Network (CAGCN), that goes beyond 1-Weisfeiler-Lehman(1-WL) test
in distinguishing non-bipartite-subgraph-isomorphic graphs. Experiments on six
benchmark datasets show that the best CAGCN variant outperforms the most
representative GNN-based recommendation model, LightGCN, by nearly 10% in
Recall@20 and also achieves around 80% speedup. Our code is publicly available
at https://github.com/YuWVandy/CAGCN.
