Inductive link prediction for knowledge graph aims at predicting missing
links between unseen entities, those not shown in training stage. Most previous
works learn entity-specific embeddings of entities, which cannot handle unseen
entities. Recent several methods utilize enclosing subgraph to obtain inductive
ability. However, all these works only consider the enclosing part of subgraph
without complete neighboring relations, which leads to the issue that partial
neighboring relations are neglected, and sparse subgraphs are hard to be
handled. To address that, we propose Subgraph Neighboring Relations Infomax,
SNRI, which sufficiently exploits complete neighboring relations from two
aspects: neighboring relational feature for node feature and neighboring
relational path for sparse subgraph. To further model neighboring relations in
a global way, we innovatively apply mutual information (MI) maximization for
knowledge graph. Experiments show that SNRI outperforms existing state-of-art
methods by a large margin on inductive link prediction task, and verify the
effectiveness of exploring complete neighboring relations in a global way to
characterize node features and reason on sparse subgraphs.

本研究提出了一种利用相邻子图信息来求解知识图谱中未观察实体之间关系的方法，其中利用相邻关系来强化节点特征和稀疏子图的关系，并应用互信息最大化来全局建模相邻关系，实验证明该方法在归纳式链接预测任务上明显优于现有最先进方法，并证实了全局建模相邻关系在特征建模和稀疏子图推理中的有效性。

子图邻域关系信息最大化的归纳式知识图谱链接预测

Subgraph Neighboring Relations Infomax for Inductive Link Prediction on Knowledge Graphs

Recently, graph collaborative filtering methods have been proposed as an
effective recommendation approach, which can capture users' preference over
items by modeling the user-item interaction graphs. In order to reduce the
influence of data sparsity, contrastive learning is adopted in graph
collaborative filtering for enhancing the performance. However, these methods
typically construct the contrastive pairs by random sampling, which neglect the
neighboring relations among users (or items) and fail to fully exploit the
potential of contrastive learning for recommendation. To tackle the above
issue, we propose a novel contrastive learning approach, named
Neighborhood-enriched Contrastive Learning, named NCL, which explicitly
incorporates the potential neighbors into contrastive pairs. Specifically, we
introduce the neighbors of a user (or an item) from graph structure and
semantic space respectively. For the structural neighbors on the interaction
graph, we develop a novel structure-contrastive objective that regards users
(or items) and their structural neighbors as positive contrastive pairs. In
implementation, the representations of users (or items) and neighbors
correspond to the outputs of different GNN layers. Furthermore, to excavate the
potential neighbor relation in semantic space, we assume that users with
similar representations are within the semantic neighborhood, and incorporate
these semantic neighbors into the prototype-contrastive objective. The proposed
NCL can be optimized with EM algorithm and generalized to apply to graph
collaborative filtering methods. Extensive experiments on five public datasets
demonstrate the effectiveness of the proposed NCL, notably with 26% and 17%
performance gain over a competitive graph collaborative filtering base model on
the Yelp and Amazon-book datasets respectively. Our code is available at:
this https URL.

本文提出一种名为邻域增强对比学习（Neighborhood-enriched Contrastive Learning，简称 NCL）的方法，将邻居嵌入到对比对中，以实现在图协同过滤方法中减少数据稀疏性的效果，并在五个公共数据集上进行广泛的实验证明了该方法的有效性，尤其在 Yelp 和 Amazon-book 数据集上分别获得了 26％和 17％的性能提升。