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

Code for TPAMI2025 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.

[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"

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

Graphs in Space. Node2Vec.

Graphs in Space. Grasp. Embedding. Evaluacija (rekonstrukcija dinamickog grafa, MAP, hub, LID).

Graphs in Space. Grasp. RandW. DeepWalk. UnbiasedWalk.

Graphs in Space. Grasp. Embedding.

Graphs in Space. Graspe.

Graphs in Space. Grasp. KMeans.

Graphs in Space. Walks. DeepWalk. UnbiasedWalk. SCWalk. HubWalk. NetworkX. Word2Vec. RandW. embedding_randw. ensemble.