Dynamic Joint Variational Graph Autoencoders

10/04/2019
by   Sedigheh Mahdavi, et al.
0

Learning network representations is a fundamental task for many graph applications such as link prediction, node classification, graph clustering, and graph visualization. Many real-world networks are interpreted as dynamic networks and evolve over time. Most existing graph embedding algorithms were developed for static graphs mainly and cannot capture the evolution of a large dynamic network. In this paper, we propose Dynamic joint Variational Graph Autoencoders (Dyn-VGAE) that can learn both local structures and temporal evolutionary patterns in a dynamic network. Dyn-VGAE provides a joint learning framework for computing temporal representations of all graph snapshots simultaneously. Each auto-encoder embeds a graph snapshot based on its local structure and can also learn temporal dependencies by collaborating with other autoencoders. We conduct experimental studies on dynamic real-world graph datasets and the results demonstrate the effectiveness of the proposed method.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/22/2018

Dynamic Graph Representation Learning via Self-Attention Networks

Learning latent representations of nodes in graphs is an important and u...
research
02/15/2023

DiffSeer: Difference-based Dynamic Weighted Graph Visualization

Existing dynamic weighted graph visualization approaches rely on users' ...
research
08/29/2022

On Time and Space: An Experimental Study on Graph Structural and Temporal Encodings

Dynamic networks reflect temporal changes occurring to the graph's struc...
research
10/11/2021

Online Graph Learning in Dynamic Environments

Inferring the underlying graph topology that characterizes structured da...
research
01/04/2021

A Survey on Embedding Dynamic Graphs

Embedding static graphs in low-dimensional vector spaces plays a key rol...
research
03/02/2020

EvoNet: A Neural Network for Predicting the Evolution of Dynamic Graphs

Neural networks for structured data like graphs have been studied extens...
research
09/07/2018

dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation Learning

Learning graph representations is a fundamental task aimed at capturing ...

Please sign up or login with your details

Forgot password? Click here to reset