Selected work

Representative Papers

A Remedy for Over-Squashing in Graph Learning via Forman-Ricci Curvature based Graph-to-Hypergraph Structural Lifting

Aug 15, 2025

Graph neural networks (GNNs) suffer from the “over-squashing” problem—severe distortion of long-range node information during neighborhood aggregation—hindering effective modeling of distant dependencies. To address this, we propose a geometry-driven graph structural enhancement method: for the first time, we integrate Forman-Ricci curvature into GNN preprocessing to identify backbone substructures; guided by curvature, we perform graph coarsening and hyperedge generation to construct topology-preserving high-order hypergraph representations. Crucially, our approach enhances cross-community information flow explicitly without modifying the underlying GNN architecture. Extensive experiments on multiple benchmark datasets demonstrate that our method significantly alleviates over-squashing, yielding average accuracy improvements of 3.2–7.8% on long-range reasoning tasks—including graph classification and link prediction—thereby validating the efficacy and generalizability of geometric priors in structural optimization.

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A Tripartite Perspective on GraphRAG

Apr 28, 2025

Large language models (LLMs) suffer from frequent hallucinations, poor traceability, and outdated knowledge in knowledge-intensive domains such as healthcare. Method: This paper proposes Tripartite-GraphRAG—a novel retrieval-augmented generation framework built upon the first tripartite knowledge graph jointly modeling domain entities, ontology concepts, and raw text snippets. It employs concept anchoring for fidelity-preserving pre-analysis and formalizes prompt generation as an unsupervised node classification task—bypassing bottlenecks in traditional entity resolution and deduplication. The method integrates lexicon-guided graph construction, Markov random field–driven prompt generation, and statistics-based concept-level embedding similarity scoring. Results: On medical history analysis tasks, Tripartite-GraphRAG significantly improves prompt information density, coverage, and structural coherence; reduces prompt length and inference overhead; and enhances the consistency and reliability of LLM outputs.

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Recent publications

Latest Papers

A Remedy for Over-Squashing in Graph Learning via Forman-Ricci Curvature based Graph-to-Hypergraph Structural Lifting

Aug 15, 2025

Graph neural networks (GNNs) suffer from the “over-squashing” problem—severe distortion of long-range node information during neighborhood aggregation—hindering effective modeling of distant dependencies. To address this, we propose a geometry-driven graph structural enhancement method: for the first time, we integrate Forman-Ricci curvature into GNN preprocessing to identify backbone substructures; guided by curvature, we perform graph coarsening and hyperedge generation to construct topology-preserving high-order hypergraph representations. Crucially, our approach enhances cross-community information flow explicitly without modifying the underlying GNN architecture. Extensive experiments on multiple benchmark datasets demonstrate that our method significantly alleviates over-squashing, yielding average accuracy improvements of 3.2–7.8% on long-range reasoning tasks—including graph classification and link prediction—thereby validating the efficacy and generalizability of geometric priors in structural optimization.

0 citationsRead paper

A Tripartite Perspective on GraphRAG

Apr 28, 2025

Large language models (LLMs) suffer from frequent hallucinations, poor traceability, and outdated knowledge in knowledge-intensive domains such as healthcare. Method: This paper proposes Tripartite-GraphRAG—a novel retrieval-augmented generation framework built upon the first tripartite knowledge graph jointly modeling domain entities, ontology concepts, and raw text snippets. It employs concept anchoring for fidelity-preserving pre-analysis and formalizes prompt generation as an unsupervised node classification task—bypassing bottlenecks in traditional entity resolution and deduplication. The method integrates lexicon-guided graph construction, Markov random field–driven prompt generation, and statistics-based concept-level embedding similarity scoring. Results: On medical history analysis tasks, Tripartite-GraphRAG significantly improves prompt information density, coverage, and structural coherence; reduces prompt length and inference overhead; and enhances the consistency and reliability of LLM outputs.

0 citationsRead paper