iMoonLab/LLM4Hypergraph
The source code of ICLR 2025 "Beyond Graphs: Can Large Language Models Comprehend Hypergraphs?"
This project helps researchers and data scientists understand how large language models (LLMs) interpret complex relationships represented in hypergraphs. It takes hypergraph data described in various formats and evaluates how well LLMs can perform tasks like identifying identical structures, counting components, or finding connections. The output shows the LLM's performance on these hypergraph reasoning tasks, helping those who work with intricate network data.
No commits in the last 6 months.
Use this if you are a researcher or data scientist experimenting with how well AI models can analyze and reason about complex, multi-way relationships found in hypergraph data.
Not ideal if you need a tool to build or visualize hypergraphs for general-purpose data analysis, as its focus is specifically on LLM evaluation.
Stars
38
Forks
6
Language
Python
License
Apache-2.0
Category
Last pushed
Jan 26, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/transformers/iMoonLab/LLM4Hypergraph"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
RManLuo/reasoning-on-graphs
Official Implementation of ICLR 2024 paper: "Reasoning on Graphs: Faithful and Interpretable...
alibaba/GraphTranslator
GraphTranslator:Aligning Graph Model to Large Language Model for Open-ended Tasks
HKUDS/OpenGraph
[EMNLP'2024] "OpenGraph: Towards Open Graph Foundation Models"
HKUDS/GraphEdit
"GraphEdit: Large Language Models for Graph Structure Learning"
UCSC-REAL/DS2
[ICLR 2025] Official implementation of paper "Improving Data Efficiency via Curating LLM-Driven...