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]	-

zhh0998/dynamic-graph-embedding-method