MGitHubL / Awesome-Temporal-Graph-LearningLinks
Awesome Temporal Graph Learning is a collection of SOTA, novel temporal graph learning methods (papers, codes, and datasets).
☆73Updated last month
Alternatives and similar repositories for Awesome-Temporal-Graph-Learning
Users that are interested in Awesome-Temporal-Graph-Learning are comparing it to the libraries listed below
Sorting:
- A Library for Dynamic Graph Learning (NeurIPS 2023)☆253Updated last year
- This repository contains the resources on graph neural network (GNN) considering heterophily.☆262Updated 7 months ago
- [WSDM 2024] GAD-NR : Graph Anomaly Detection via Neighborhood Reconstruction☆45Updated 7 months ago
- Code & data for AAAI'23 Oral paper "Heterogeneous Graph Masked Autoencoders".☆62Updated 2 years ago
- A Survey of Learning from Graphs with Heterophily☆144Updated 4 months ago
- NeurIPS 2022, Revisiting Heterophily For Graph Neural Networks, official PyTorch implementation for Adaptive Channel Mixing (ACM) GNN fra…☆84Updated 7 months ago
- ☆17Updated 2 weeks ago
- A Critical Look at the Evaluation of GNNs under Heterophily: Are We Really Making Progress?☆119Updated 2 years ago
- Advances on machine learning of dynamic (temporal) graphs, covering the reading list of recent top academic conferences.☆62Updated last year
- A curated list of papers on graph structure learning (GSL).☆49Updated 6 months ago
- [NeurIPS 2021] Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods☆123Updated 2 years ago
- An Empirical Evaluation of Temporal Graph Benchmark☆35Updated last year
- ☆47Updated 11 months ago
- Dynamic Graph Benchmark☆81Updated 2 years ago
- A collection of papers on Graph Structural Learning (GSL)☆55Updated last year
- ☆135Updated last year
- ☆75Updated 2 years ago
- [WWW'22] Towards Unsupervised Deep Graph Structure Learning☆142Updated 2 years ago
- "GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection" in NeurIPS 2023☆128Updated last year
- ☆115Updated last year
- A collection of papers for graph anomaly detection, and published algorithms and datasets.☆127Updated last year
- TREND: TempoRal Event and Node Dynamics for Graph Representation Learning. WWW-2022.☆37Updated last year
- [Neurips 2024] Disentangled Graph Homophily☆26Updated 5 months ago
- ☆101Updated last year
- Reimplementation of AAAI21 paper "Beyond Low-frequency Information in Graph Convolutional Networks" based on PyTorch and PyTorch Geometri…☆23Updated 2 years ago
- Advances on machine learning of graphs, covering the reading list of recent top academic conferences.☆200Updated 3 weeks ago
- ☆19Updated 6 months ago
- ☆56Updated 9 months ago
- ☆178Updated last year
- ☆29Updated last year