Stars
Awesome-GraphRAG: A curated list of resources (surveys, papers, benchmarks, and opensource projects) on graph-based retrieval-augmented generation.
Official repository for the Topological Deep Learning Challenge 2024, organized by TAG-DS & PyT-Team and hosted by GRaM Workshop @ ICML 2024.
[AAAI 2024] Weisfeiler and Lehman Go Paths: Learning Topological Features via Path Complexes
Causal Discovery from Nonstationary/Heterogeneous Data.
Causal Discovery in Python. Learning causality from data.
Paper list about hyperbolic embedding, hyperbolic models,hyperbolic applications
The essence of my research, distilled for reusability. Enjoy 🥃!
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
Code Repository for the paper "GEFL: Extended Filtration Learning for Graph Classification" (LoG 2022)
All graph/GNN papers accepted at NeurIPS 2024.
It is a comprehensive resource hub compiling all graph papers accepted at the International Conference on Learning Representations (ICLR) in 2025.
All graph/GNN papers accepted at the International Conference on Machine Learning (ICML) 2024.
[NeurIPS 2020] "Graph Contrastive Learning with Augmentations" by Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen
This repository contains the implementation for our work "TopoDiffusionNet: A Topology-aware Diffusion Model", accepted to ICLR 2025.
awesome-topology-driven-deep-image-analysis
Adversarial Graph Augmentation to Improve Graph Contrastive Learning
[NeurIPS 2023] Implementation of "Improving Self-supervised Molecular Representation Learning using Persistent Homology"
Advances on machine learning of graphs, covering the reading list of recent top academic conferences.
[NeurIPS 2025 D&B] Open-source Multi-agent Poster Generation from Papers
从无名小卒到大模型（LLM）大英雄~ 欢迎关注后续！！！
The latest research progress of Contrastive Learning(CL), Data Augmentation(DA) and Self-Supervised Learning(SSL) in Recommender Systems
A collection of graph foundation models including papers, codes, and datasets.
Awesome literature on imbalanced learning on graphs
A collection of AWESOME things about Graph-Related LLMs.