  Graphs can represent relational information among entities and graph
structures are widely used in many intelligent tasks such as search,
recommendation, and question answering. However, most of the graph-structured
data in practice suffers from incompleteness, and thus link prediction becomes
an important research problem. Though many models are proposed for link
prediction, the following two problems are still less explored: (1) Most
methods model each link independently without making use of the rich
information from relevant links, and (2) existing models are mostly designed
based on associative learning and do not take reasoning into consideration.
With these concerns, in this paper, we propose Graph Collaborative Reasoning
(GCR), which can use the neighbor link information for relational reasoning on
graphs from logical reasoning perspectives. We provide a simple approach to
translate a graph structure into logical expressions, so that the link
prediction task can be converted into a neural logic reasoning problem. We
apply logical constrained neural modules to build the network architecture
according to the logical expression and use back propagation to efficiently
learn the model parameters, which bridges differentiable learning and symbolic
reasoning in a unified architecture. To show the effectiveness of our work, we
conduct experiments on graph-related tasks such as link prediction and
recommendation based on commonly used benchmark datasets, and our graph
collaborative reasoning approach achieves state-of-the-art performance.
