# Subgraph Neighboring Relations Infomax for Inductive Link Prediction on Knowledge Graphs arXiv: https://arxiv.org/abs/2208.00850 ยท HTML: https://mlsanity.com/paper/2208.00850/ ## Key facts - Cited by 27 papers in the map (data through 14 Oct 2026). - Builds on 20 of its 30 references that are in the map. - First posted on arXiv 28 Jul 2022. ## Builds on (most cited first, 20 of 20) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019) - [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014) - [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017) - [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017) - [Deep Graph Infomax](https://mlsanity.com/paper/1809.10341/) (2018) - [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014) - [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018) - [Convolutional 2D Knowledge Graph Embeddings](https://mlsanity.com/paper/1707.01476/) (2017) - [GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training](https://mlsanity.com/paper/2006.09963/) (2020) - [Composition-based Multi-Relational Graph Convolutional Networks](https://mlsanity.com/paper/1911.03082/) (2019) - [Inductive Relation Prediction by Subgraph Reasoning](https://mlsanity.com/paper/1911.06962/) (2019) - [DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs](https://mlsanity.com/paper/1911.00055/) (2019) - [Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs](https://mlsanity.com/paper/1705.05742/) (2017) - [RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems](https://mlsanity.com/paper/1803.03467/) (2018) - [SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism](https://mlsanity.com/paper/2101.08170/) (2021) - [Communicative Message Passing for Inductive Relation Reasoning](https://mlsanity.com/paper/2012.08911/) (2020) - [Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embedding](https://mlsanity.com/paper/1811.01399/) (2018) - [Topology-Aware Correlations Between Relations for Inductive Link Prediction in Knowledge Graphs](https://mlsanity.com/paper/2103.03642/) (2021) ## Built on by (most cited first, 27 of 27) - [Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning](https://mlsanity.com/paper/2310.01061/) (2023) - [A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal](https://mlsanity.com/paper/2212.05767/) (2022) - [InGram: Inductive Knowledge Graph Embedding via Relation Graphs](https://mlsanity.com/paper/2305.19987/) (2023) - [Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer](https://mlsanity.com/paper/2303.03922/) (2023) - [Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphs](https://mlsanity.com/paper/2302.01859/) (2023) - [Message Intercommunication for Inductive Relation Reasoning](https://mlsanity.com/paper/2305.14074/) (2023) - [Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction Prediction](https://mlsanity.com/paper/2312.06682/) (2023) - [Towards Few-shot Inductive Link Prediction on Knowledge Graphs: A Relational Anonymous Walk-guided Neural Process Approach](https://mlsanity.com/paper/2307.01204/) (2023) - [Towards Enhancing Relational Rules for Knowledge Graph Link Prediction](https://mlsanity.com/paper/2310.13411/) (2023) - [Learning Rule-Induced Subgraph Representations for Inductive Relation Prediction](https://mlsanity.com/paper/2408.07088/) (2024) - [Customized Subgraph Selection and Encoding for Drug-drug Interaction Prediction](https://mlsanity.com/paper/2411.01535/) (2024) - [Beyond Transduction: A Survey on Inductive, Few Shot, and Zero Shot Link Prediction in Knowledge Graphs](https://mlsanity.com/paper/2312.04997/) (2023) - [NeuralKG-ind: A Python Library for Inductive Knowledge Graph Representation Learning](https://mlsanity.com/paper/2304.14678/) (2023) - [Do as I can, not as I get](https://mlsanity.com/paper/2306.10345/) (2023) - [Towards Better Benchmark Datasets for Inductive Knowledge Graph Completion](https://mlsanity.com/paper/2406.11898/) (2024) - [Expanding the Scope: Inductive Knowledge Graph Reasoning with Multi-Starting Progressive Propagation](https://mlsanity.com/paper/2407.10430/) (2024) - [StaR-KVQA: Structured Reasoning Traces for Implicit-Knowledge Visual Question Answering](https://mlsanity.com/paper/2510.06638/) (2025) - [Learning Complete Topology-Aware Correlations Between Relations for Inductive Link Prediction](https://mlsanity.com/paper/2309.11528/) (2023) - [One Subgraph for All: Efficient Reasoning on Opening Subgraphs for Inductive Knowledge Graph Completion](https://mlsanity.com/paper/2404.15807/) (2024) - [AMCEN: An Attention Masking-based Contrastive Event Network for Two-stage Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2405.10346/) (2024) - [Reevaluation of Inductive Link Prediction](https://mlsanity.com/paper/2409.20130/) (2024) - [S2DN: Learning to Denoise Unconvincing Knowledge for Inductive Knowledge Graph Completion](https://mlsanity.com/paper/2412.15822/) (2024) - [PathE: Leveraging Entity-Agnostic Paths for Parameter-Efficient Knowledge Graph Embeddings](https://mlsanity.com/paper/2501.19095/) (2025) - [Model Graph Inductive Learning for Knowledge Graph Completion](https://mlsanity.com/paper/2606.16509/) (2026) - [Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Survey](https://mlsanity.com/paper/2607.09666/) (2026) - [PEARL: Path-Entity Aligned Relational Learning with Contextual Subgraphs for Inductive Knowledge Graph Completion](https://mlsanity.com/paper/2609.02216/) (2026) - [QORH: Learning Query-Specific Ordered Rule Hypergraphs for Inductive Knowledge Graph Link Prediction](https://mlsanity.com/paper/2610.18198/) (2026) ## Abstract (arXiv) 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.