Graph Collaborative Reasoning
Hanxiong Chen, Yunqi Li, Shaoyun Shi, Shuchang Liu, He Zhu and, Yongfeng Zhang

TL;DR
This paper introduces Graph Collaborative Reasoning (GCR), a novel approach that combines logical reasoning with neural networks to improve link prediction and recommendation tasks on incomplete graph data.
Contribution
GCR translates graph structures into logical expressions, enabling relational reasoning that leverages neighbor link information and unifies differentiable learning with symbolic reasoning.
Findings
Achieves state-of-the-art performance on benchmark datasets.
Effectively models relational information through logical reasoning.
Outperforms existing methods in link prediction and recommendation.
Abstract
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…
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