/dynamic-graph-embedding-method

dynamic graph/network embedding/representation methods

dynamic-graph-embedding-method

This page is to summarize important methods about dynamic graph embedding/representation or dynamic network embedding/representation.

Method Published Code Description
TGAT: Inductive representation learning on temporal graphs ICLR 20 [pytorch] -
DySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention Networks WSDM 20 [tensorflow & data] Attributed
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs AAAI 20 [pytorch & data] Attributed
DyREP: Learning Representations over Dynamic Graphs ICLR 19 - -
MMDNE: Temporal Network Embedding with Micro- and Macro-dynamics CIKM 19 [pytorch & data] -
JODIE: Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks KDD 19 [python & data] Heterogeneous
node2bits: Compact Time- and Attribute-aware Node Representations for User Stitching ECML PKDD 19 [python & data] Attributed & Heterogeneous
tNodeEmbed: Node Embedding over Temporal Graphs IJCAI 19 [keras] -
DynamicTriad: Dynamic Network Embedding by Modeling Triadic Closure Process AAAI 18 [python27 & data] -
DynGEM: Deep Embedding Method for Dynamic Graphs IJCAI 17 workshop - -
DNPS: Modeling Large-Scale Dynamic Social Networks via Node Embeddings TKDE 18 [None] -
TNE: Scalable Temporal Latent Space Inference for Link Prediction in Dynamic Social Networks TKDE 16 [c/c++ & data] -