Awesome papers about machine learning (deep learning) on dynamic (temporal) graphs (networks / knowledge graphs).

Code for paper "EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph Learning"

CTGCN: k-core based Temporal Graph Convolutional Network for Dynamic Graphs (accepted by IEEE TKDE in 2020) https://ieeexplore.ieee.org/document/9240056

GloDyNE: Global Topology Preserving Dynamic Network Embedding (accepted by IEEE TKDE in 2020) https://ieeexplore.ieee.org/document/9302718

[TKDE 2022] The source code of "Dynamic Graph Neural Networks for Sequential Recommendation"

Advances on machine learning of dynamic (temporal) graphs, covering the reading list of recent top academic conferences.

[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.

Code for "DyGCN: Dynamic Graph Embedding with Graph Convolutional Network"