Stars
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. It also includes (conditional) independence tests and score functions.
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.
PyTorch tutorials, examples and some books I found 【不定期更新】整理的PyTorch 最新版教程、例子和书籍