Representation learning on dynamic graphs using self-attention networks
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Updated
Mar 24, 2023 - Python
Representation learning on dynamic graphs using self-attention networks
Variational Graph Recurrent Neural Networks - PyTorch
CTGCN: k-core based Temporal Graph Convolutional Network for Dynamic Graphs (accepted by IEEE TKDE in 2020) https://ieeexplore.ieee.org/document/9240056
Linked Dynamic Graph CNN: Learning through Point Cloud by Linking Hierarchical Features
[AAAI 2023] Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks
[NeurIPS 2022] The official PyTorch implementation of "Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs"
[ACM Computing Surveys'23] Implementations or refactor of some temporal link prediction/dynamic link prediction methods and summary of related open resources for survey paper "Temporal Link Prediction: A Unified Framework, Taxonomy, and Review" which has been accepted by ACM Computing Surveys.
[ICDM 2020] Python implementation for "Dynamic Graph Collaborative Filtering."
Code for "Graph Neural Networks for Friend Ranking in Large-scale Social Platforms" (WWW 2021).
DYnamic MOtif-NoDes (DYMOND) is a dynamic network generative model based on temporal motifs and node behavior.
Python 3 supported version for DySAT
Representation and learning framework for dynamic graphs using Graph Neural Networks.
The official repository for the paper "Deep learning for dynamic graphs: models and benchmarks" accepted at IEEE TNNLS
Official reference implementation of our paper "Temporal Graph ODEs for Irregularly-Sampled Time Series" accepted at IJCAI 24
dynnode2vec is a python package that implements algorithms to embed dynamic graphs
[TKDE'23] Demo code of the paper entitled "High-Quality Temporal Link Prediction for Weighted Dynamic Graphs via Inductive Embedding Aggregation", which has been accepted by IEEE TKDE
Implementation codes for NeurIPS23 paper "Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts"
Straph is a Python package for the modelisation, analysis and visualisation of Stream Graphs (https://arxiv.org/abs/1710.04073).
Implementation codes for KDD24 paper "LLM4DyG: Can Large Language Models Solve Spatial-Temporal Problems on Dynamic Graphs?"
Official reference implementation of our paper "Long Range Propagation on Continuous-Time Dynamic Graphs" accepted at ICML24 and "Effective Non-Dissipative Propagation for Continuous-Time Dynamic Graphs" accepted at Temporal Graph Learning Workshop @ NeurIPS 2023
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