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

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

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

Code and dataset for IEEE TKDE paper "Dynamic Heterogeneous Information Network Embedding with Meta-path based Proximity"

Source code for CIKM 2019 paper "Temporal Network Embedding with Micro- and Macro-dynamics"

SG-EDNE: Skip-Gram based Ensembles Dynamic Network Embedding (for our paper "Robust Dynamic Network Embedding via Ensembles")

[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

Compact time- and attribute-aware node representations

DANTE is a software tool for pairwise alignment of dynamic networks. It computes the topological node similarities via temporal embedding.

Codebase for simulating and estimating the Attractor-Based Coevolving Dot Product Random Graph Model (ABCDPRGM), a dynamic network model for analyzing polarization and flocking in graph data. Includes synthetic experiments and real-data analysis using Age of Empires IV ranked match data.