Lorenz Wendlinger, Simon Alexander Nonn, Abdullah Al Zubaer, Michael Granitzer
2602.04812v1 | PDF
February 4, 2026
The paper improves legal citation link prediction using Graph Neural Networks (GNNs). The authors introduce R-HGE (Robust Heterogeneous Graph Enrichment), which predicts missing citations between legal cases and laws more accurately than previous methods.
Graph Neural Networks (GNNs) are deep learning models that operate on graph-structured data (nodes + edges) by iteratively passing messages between connected nodes to learn representations. After multiple rounds of neighborhood aggregation, each node captures information from its surrounding structure, enabling tasks like node classification, link prediction, and graph-level classification.
Robust Heterogeneous Graph Enrichment extends basic GNNs to handle real-world graphs that have multiple node/edge types (heterogeneous), missing information (enrichment fills gaps with external data or inferred connections), and noise or incompleteness (robustness). It’s particularly relevant for domains like legal AI where knowledge graphs naturally contain diverse entity types, incomplete relationships, and messy data.