# CaseLink: Inductive Graph Learning for Legal Case Retrieval

arXiv: https://arxiv.org/abs/2403.17780 · HTML: https://mlsanity.com/paper/2403.17780/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2024.

## Built on by (most cited first, 11 of 11)

- [Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learning](https://mlsanity.com/paper/2501.01124/) (2025)
- [IL-PCSR: Legal Corpus for Prior Case and Statute Retrieval](https://mlsanity.com/paper/2511.00268/) (2025)
- [Text Meets Topology: Rethinking Out-of-distribution Detection in Text-Rich Networks](https://mlsanity.com/paper/2508.17690/) (2025)
- [LLM-based Embedders for Prior Case Retrieval](https://mlsanity.com/paper/2507.18455/) (2025)
- [Segment First, Retrieve Better: Realistic Legal Search via Rhetorical Role-Based Queries](https://mlsanity.com/paper/2508.00679/) (2025)
- [Does Homophily Help in Robust Test-time Node Classification?](https://mlsanity.com/paper/2510.22289/) (2025)
- [NeuroSymbolic AI for Legal AI-TRISM: Trustworthy, Reliable, Interpretable, Safe Models](https://mlsanity.com/paper/2606.15646/) (2026)
- [Call Neighbours Yourself: Graph Walks with Destination-Conditioned On-Policy Self-Distillation](https://mlsanity.com/paper/2608.29588/) (2026)
- [Learning from Litigation: Graphs and LLMs for Retrieval and Reasoning in eDiscovery](https://mlsanity.com/paper/2405.19164/) (2024)
- [GLIER: Generative Legal Inference and Evidence Ranking for Legal Case Retrieval](https://mlsanity.com/paper/2604.23779/) (2026)
- [Trustworthy Domain-Specific AI for Structured Knowledge Retrieval and Reasoning](https://mlsanity.com/paper/2610.08894/) (2026)

## Abstract (arXiv)

In case law, the precedents are the relevant cases that are used to support the decisions made by the judges and the opinions of lawyers towards a given case. This relevance is referred to as the case-to-case reference relation. To efficiently find relevant cases from a large case pool, retrieval tools are widely used by legal practitioners. Existing legal case retrieval models mainly work by comparing the text representations of individual cases. Although they obtain a decent retrieval accuracy, the intrinsic case connectivity relationships among cases have not been well exploited for case encoding, therefore limiting the further improvement of retrieval performance. In a case pool, there are three types of case connectivity relationships: the case reference relationship, the case semantic relationship, and the case legal charge relationship. Due to the inductive manner in the task of legal case retrieval, using case reference as input is not applicable for testing. Thus, in this paper, a CaseLink model based on inductive graph learning is proposed to utilise the intrinsic case connectivity for legal case retrieval, a novel Global Case Graph is incorporated to represent both the case semantic relationship and the case legal charge relationship. A novel contrastive objective with a regularisation on the degree of case nodes is proposed to leverage the information carried by the case reference relationship to optimise the model. Extensive experiments have been conducted on two benchmark datasets, which demonstrate the state-of-the-art performance of CaseLink. The code has been released on https://github.com/yanran-tang/CaseLink.