[TPAMI 2024] Awesome Resources of GNNs for Time Series Analysis (GNN4TS)

Archive of Temporal Knowledge Reasoning in Social Network and Knowledge Graph

Representation learning on dynamic graphs using self-attention networks

A collection of resources on dynamic/streaming/temporal/evolving graph processing systems, databases, data structures, datasets, and related academic and industrial work

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

A paper list of research about social knowledge graph

[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).

Anomaly Detection in Dynamic Graphs

Source code of "RapidFlow: An Efficient Approach to Continuous Subgraph Matching" published in VLDB'2022 - By Shixuan Sun, Xibo Sun, Bingsheng He and Qiong Luo

Official reference implementation of our paper "Temporal Graph ODEs for Irregularly-Sampled Time Series" accepted at IJCAI 24

Visual Analysis of Temporal Summaries in Dynamic Graphs (IEEE TVCG)

DYnamic MOtif-NoDes (DYMOND) is a dynamic network generative model based on temporal motifs and node behavior.

The official repository for the paper "Deep learning for dynamic graphs: models and benchmarks" accepted at IEEE TNNLS

Python 3 supported version for DySAT