Representation learning on dynamic graphs using self-attention networks
-
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
[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.
[NeurIPS 2022] The official PyTorch implementation of "Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs"
[AAAI 2023] Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks
[ICDM 2020] Python implementation for "Dynamic Graph Collaborative Filtering."
Code for "Graph Neural Networks for Friend Ranking in Large-scale Social Platforms" (WWW 2021).
Straph is a Python package for the modelisation, analysis and visualisation of Stream Graphs (https://arxiv.org/abs/1710.04073).
[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
DYnamic MOtif-NoDes (DYMOND) is a dynamic network generative model based on temporal motifs and node behavior.
dynnode2vec is a python package that implements algorithms to embed dynamic graphs
Official reference implementation of our paper "Temporal Graph ODEs for Irregularly-Sampled Time Series" accepted at IJCAI 24
The code for our ICLR 2024 paper: "Beyond Spatio-Temporal Representations: Evolving Fourier Transform for Temporal Graphs"
Implementation codes for KDD24 paper "LLM4DyG: Can Large Language Models Solve Spatial-Temporal Problems on Dynamic Graphs?"
Given a temporal network, performs Temporal Random Walk using different sampling strategies.
PyTorch Implementation of a Deep Learning Model for Temporal Link Prediction in MANETs
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
Add a description, image, and links to the dynamic-graphs topic page so that developers can more easily learn about it.
To associate your repository with the dynamic-graphs topic, visit your repo's landing page and select "manage topics."