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.

本研究提出了一个名为图形协作推理（GCR）的方法，该方法利用邻居链接信息从逻辑推理角度对图形进行关系推理，并在常用基准数据集上展示其在链接预测等任务方面的卓越表现。

图协同推理

Graph Collaborative Reasoning

Existing Collaborative Filtering (CF) methods are mostly designed based on
the idea of matching, i.e., by learning user and item embeddings from data
using shallow or deep models, they try to capture the associative relevance
patterns in data, so that a user embedding can be matched with relevant item
embeddings using designed or learned similarity functions. However, as a
cognition rather than a perception intelligent task, recommendation requires
not only the ability of pattern recognition and matching from data, but also
the ability of cognitive reasoning in data. In this paper, we propose to
advance Collaborative Filtering (CF) to Collaborative Reasoning (CR), which
means that each user knows part of the reasoning space, and they collaborate
for reasoning in the space to estimate preferences for each other. Technically,
we propose a Neural Collaborative Reasoning (NCR) framework to bridge learning
and reasoning. Specifically, we integrate the power of representation learning
and logical reasoning, where representations capture similarity patterns in
data from perceptual perspectives, and logic facilitates cognitive reasoning
for informed decision making. An important challenge, however, is to bridge
differentiable neural networks and symbolic reasoning in a shared architecture
for optimization and inference. To solve the problem, we propose a modularized
reasoning architecture, which learns logical operations such as AND ($\wedge$),
OR ($\vee$) and NOT ($\neg$) as neural modules for implication reasoning
($\rightarrow$). In this way, logical expressions can be equivalently organized
as neural networks, so that logical reasoning and prediction can be conducted
in a continuous space. Experiments on real-world datasets verified the
advantages of our framework compared with both shallow, deep and reasoning
models.

本文提出了一种新的协作推荐系统框架，即协作推理 (CR)，将学习和推理融为一体。该框架通过一种神经模块化的形式，将符号推理操作嵌入到基于神经网络的模型中，将逻辑表达式等价地组织为神经网络，从而实现了在连续空间中进行逻辑推理和预测。实验证明，与现有的浅层、深层和推理模型相比，本文提出的框架具有更好的推荐性能。