Ongoing project: a library for graph foundation model

Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs

Universal Graph Transformer Self-Attention Networks (TheWebConf WWW 2022) (Pytorch and Tensorflow)

GraphACL: Simple and Asymmetric Graph Contrastive Learning (NeurIPS 2023)

Paper List of Pre-trained Foundation Recommender Models

"OpenGraph: Towards Open Graph Foundation Models"

Paper List for Fair Graph Learning (FairGL).

[IJCAI 2024] Papers about graph reduction including graph coarsening, graph condensation, graph sparsification, graph summarization, etc.

A curated collection of research papers exploring the utilization of LLMs for graph-related tasks.

Code for TKDE paper "Self-supervised learning on graphs: Contrastive, generative, or predictive"

Advances on machine learning of dynamic (temporal) graphs, covering the reading list of recent top academic conferences.

A curated list of papers and resources based on "Large Language Models on Graphs: A Comprehensive Survey".

Edgeformers: Graph-Empowered Transformers for Representation Learning on Textual-Edge Networks (ICLR 2023)

[ICML 2023] An official source code for paper "Dink-Net: Neural Clustering on Large Graphs".

Official Implementation of AdaGCN (ICLR 2021)

[WWW'2024] "RLMRec: Representation Learning with Large Language Models for Recommendation"

[SIGIR'2023] "DCCF: Disentangled Contrastive Collaborative Filtering"

Awesome Papers About Performing Prompting On Graphs

GraphENS: Neighbor-Aware Ego Network Synthesis for Class-Imbalanced Node Classification (ICLR'22)

PyTorch implementation of GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks

Read and write Tensorflow TFRecord data from Apache Spark.

🐫 CAMEL: Finding the Scaling Law of Agents. A multi-agent framework. https://www.camel-ai.org

Papers about out-of-distribution generalization on graphs.

General Strategy for Unlearning in Graph Neural Networks