Our GitHub repo Awesome-Graph-LLM: github.com/XiaoxinHe/Awes…, has received 1k+ stars! 🌟A huge thank you to everyone who has supported and contributed. Let's keep pushing the boundaries of Awesome-Graph-LLM together!

Register for the workshop on LLMs, GNNs and Foundation Models -- it will be exciting!
The Web Conference #WebConference2024 👇

Neural Network Diffusion

Diffusion models have achieved remarkable success in image and video generation. In this work, we demonstrate that diffusion models can also generate high-performing neural network parameters. Our approach is simple, utilizing an autoencoder and a

Check out the latest work at NeurIPS 2023 on learning on graphs: github.com/XiaoxinHe/neur…

Presenting the arxiv-2023 dataset!

We gathered all CS arXiv papers from 2023, exceeding GPT-3.5's knowledge cutoff, totaling 57,471 papers and 122,835 connections!

Dataset: github.com/XiaoxinHe/arxi…
Paper: arxiv.org/abs/2305.19523

Dive into ICML 2023 papers effortlessly! 🧐
Introducing a handy toy tool for paper collection and filtering. Whether you want the full list or targeted topics, this GitHub repo has got you covered: github.com/XiaoxinHe/icml…

Excited to present our poster tomorrow! Please come and join the discussion😁

New paper w/ Xiaoxin He, T. Laurent, Bryan Hooi 'Explanations as Features: LLM-Based Features for Text-Attributed Graphs'
ArXiv: arxiv.org/pdf/2305.19523…
GitHub: github.com/XiaoxinHe/TAPE

🚀Awesome-Graph-LLM

Check out this GitHub repo for a collection of AWESOME things about Graph-Related Large Language Models (LLMs): github.com/XiaoxinHe/Awes…

My talk slides for the IPAM workshop 'Learning and Emergence in Molecular Systems'
dropbox.com/s/2mnaiiq295bo…