# TRIX: A More Expressive Model for Zero-shot Domain Transfer in Knowledge Graphs

arXiv: https://arxiv.org/abs/2502.19512 · HTML: https://mlsanity.com/paper/2502.19512/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 57 references that are in the map.
- First posted on arXiv 26 Feb 2025.

## Builds on (most cited first, 30 of 41)

- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding](https://mlsanity.com/paper/2308.14508/) (2023)
- [Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks](https://mlsanity.com/paper/1810.02244/) (2018)
- [Convolutional 2D Knowledge Graph Embeddings](https://mlsanity.com/paper/1707.01476/) (2017)
- [KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation](https://mlsanity.com/paper/1911.06136/) (2019)
- [Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting](https://mlsanity.com/paper/2006.09252/) (2020)
- [Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction](https://mlsanity.com/paper/2106.06935/) (2021)
- [DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning](https://mlsanity.com/paper/1707.06690/) (2017)
- [Inductive Relation Prediction by Subgraph Reasoning](https://mlsanity.com/paper/1911.06962/) (2019)
- [ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding](https://mlsanity.com/paper/2305.14196/) (2023)
- [DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs](https://mlsanity.com/paper/1911.00055/) (2019)
- [Equivariant Subgraph Aggregation Networks](https://mlsanity.com/paper/2110.02910/) (2021)
- [Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning](https://mlsanity.com/paper/2010.16103/) (2020)
- [Rethinking the Expressive Power of GNNs via Graph Biconnectivity](https://mlsanity.com/paper/2301.09505/) (2023)
- [RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs](https://mlsanity.com/paper/2010.04029/) (2020)
- [Knowledge Transfer for Out-of-Knowledge-Base Entities: A Graph Neural Network Approach](https://mlsanity.com/paper/1706.05674/) (2017)
- [CoDEx: A Comprehensive Knowledge Graph Completion Benchmark](https://mlsanity.com/paper/2009.07810/) (2020)
- [On Learning Sets of Symmetric Elements](https://mlsanity.com/paper/2002.08599/) (2020)
- [Towards Foundation Models for Knowledge Graph Reasoning](https://mlsanity.com/paper/2310.04562/) (2023)
- [Graph Neural Networks for Link Prediction with Subgraph Sketching](https://mlsanity.com/paper/2209.15486/) (2022)
- [Commonsense Knowledge Base Completion with Structural and Semantic Context](https://mlsanity.com/paper/1910.02915/) (2019)
- [InGram: Inductive Knowledge Graph Embedding via Relation Graphs](https://mlsanity.com/paper/2305.19987/) (2023)
- [Making Large Language Models Perform Better in Knowledge Graph Completion](https://mlsanity.com/paper/2310.06671/) (2023)
- [Exploring Large Language Models for Knowledge Graph Completion](https://mlsanity.com/paper/2308.13916/) (2023)
- [Equivariant Polynomials for Graph Neural Networks](https://mlsanity.com/paper/2302.11556/) (2023)
- [Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations](https://mlsanity.com/paper/2110.02834/) (2021)
- [Improving Knowledge Graph Embedding Using Simple Constraints](https://mlsanity.com/paper/1805.02408/) (2018)
- [KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion](https://mlsanity.com/paper/2402.02389/) (2024)

## Built on by (most cited first, 24 of 24)

- [Equivariance Everywhere All At Once: A Recipe for Graph Foundation Models](https://mlsanity.com/paper/2506.14291/) (2025)
- [Flock: A Knowledge Graph Foundation Model via Learning on Random Walks](https://mlsanity.com/paper/2510.01510/) (2025)
- [SEMMA: A Semantic Aware Knowledge Graph Foundation Model](https://mlsanity.com/paper/2505.20422/) (2025)
- [GraphOracle: Efficient Fully-Inductive Knowledge Graph Reasoning via Relation-Dependency Graphs](https://mlsanity.com/paper/2505.11125/) (2025)
- [HYPER: A Foundation Model for Inductive Link Prediction with Knowledge Hypergraphs](https://mlsanity.com/paper/2506.12362/) (2025)
- [Bridging Input Feature Spaces Towards Graph Foundation Models](https://mlsanity.com/paper/2605.04834/) (2026)
- [RelAgent: LLM Agents as Data Scientists for Relational Learning](https://mlsanity.com/paper/2605.07840/) (2026)
- [KGPFN: Unlocking the Potential of Knowledge Graph Foundation Model via In-Context Learning](https://mlsanity.com/paper/2605.14907/) (2026)
- [Designing a reliable lateral movement detector using a graph foundation model](https://mlsanity.com/paper/2504.13527/) (2025)
- [Knowledge Reasoning Language Model: Unifying Knowledge and Language for Inductive Knowledge Graph Reasoning](https://mlsanity.com/paper/2510.13909/) (2025)
- [Geometric Structural Knowledge Graph Foundation Model](https://mlsanity.com/paper/2512.22931/) (2025)
- [Breaking the Reasoning Horizon in Entity Alignment Foundation Models](https://mlsanity.com/paper/2601.21174/) (2026)
- [Graphlets as Building Blocks for Structural Vocabulary in Knowledge Graph Foundation Models](https://mlsanity.com/paper/2605.06154/) (2026)
- [Half a Link can Be Enough to Predict a Whole Link: Understanding Generalization in Knowledge Graph Foundation Models](https://mlsanity.com/paper/2606.18001/) (2026)
- [KGFR: A Foundation Retriever for Generalized Knowledge Graph Question Answering](https://mlsanity.com/paper/2511.04093/) (2025)
- [Boosting Knowledge Graph Foundation Models via Enhanced Negative Sampling](https://mlsanity.com/paper/2605.27023/) (2026)
- [Harnessing Structural Context for Entity Alignment Foundation Models](https://mlsanity.com/paper/2606.06109/) (2026)
- [GRATE: Temporal Extensions for Inductive KG Foundation Models via Gated Rotary Attention](https://mlsanity.com/paper/2607.10197/) (2026)
- [Neural Message Passing on Structural Interaction Graphs for Fully-Inductive Graph Neural Networks](https://mlsanity.com/paper/2608.08567/) (2026)
- [FITTER: Vocabulary-Agnostic Cross-Domain Inference on Temporal Knowledge Graphs](https://mlsanity.com/paper/2608.10668/) (2026)
- [Structure-Internalized Rule Language Model for Faithful Knowledge Graph Reasoning](https://mlsanity.com/paper/2608.17443/) (2026)
- [Reification as a Transferable Vocabulary: Zero-Shot Link Prediction with Vanilla GNNs](https://mlsanity.com/paper/2609.11347/) (2026)
- [The Universal Classifier for Graph Learning](https://mlsanity.com/paper/2609.36302/) (2026)
- [MERIT: Scaling KG Foundation Models with Relation Transformers](https://mlsanity.com/paper/2610.15638/) (2026)

## Abstract (arXiv)

Fully inductive knowledge graph models can be trained on multiple domains and subsequently perform zero-shot knowledge graph completion (KGC) in new unseen domains. This is an important capability towards the goal of having foundation models for knowledge graphs. In this work, we introduce a more expressive and capable fully inductive model, dubbed TRIX, which not only yields strictly more expressive triplet embeddings (head entity, relation, tail entity) compared to state-of-the-art methods, but also introduces a new capability: directly handling both entity and relation prediction tasks in inductive settings. Empirically, we show that TRIX outperforms the state-of-the-art fully inductive models in zero-shot entity and relation predictions in new domains, and outperforms large-context LLMs in out-of-domain predictions. The source code is available at https://github.com/yuchengz99/TRIX.