Papers by Jiafan Li
Learning to Think on Hypergraph: HyperCoT for Structure-Guided N-ary Knowledge Graph Completion (2026.acl-long)
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| Challenge: | Existing methods to solve knowledge hypergraph link prediction problem are limited by their ability to generate chain-of-thought (CoT) representations. |
| Approach: | They propose a structure-aware approach that models multi-hop structural reasoning as a depth-sensitive progressive evidence accumulation process. |
| Outcome: | Experiments on three real-world datasets show that HyperCoT outperforms strong n-ary KGC baselines while yielding interpretable multi-hop reasoning traces. |
Advancement in Graph Understanding: A Multimodal Benchmark and Fine-Tuning of Vision-Language Models (2024.acl-long)
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| Challenge: | Graph data organizes complex relationships and interactions between objects . Graph neural networks (GNNs) are becoming more popular in graph learning . |
| Approach: | They propose a new paradigm for interactive and instructional graph data understanding and reasoning . they first evaluate the capabilities of public VLMs in graph learning from multiple aspects . |
| Outcome: | The proposed model achieves an accuracy increase of 5%-15% compared to baseline models . the best-performing model achieve scores comparable to Gemini in GPT-asissted Evaluation . |
SLiNT: Structure-aware Language Model with Injection and Contrastive Training for Knowledge Graph Completion (2025.findings-emnlp)
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| Challenge: | Large language models (LLMs) lack structural information and semantic context to infer missing entities . large language models often lack structural signals to infuse missing entities into knowledge graphs . |
| Approach: | a modular framework integrates structural information and semantic context into a frozen LLM backbone for link prediction. |
| Outcome: | a new framework integrates KG-derived structural information and semantic context to infer missing entities. |