Dual Graph Representation Learning

02/25/2020
by   Huiling Zhu, et al.
0

Graph representation learning embeds nodes in large graphs as low-dimensional vectors and is of great benefit to many downstream applications. Most embedding frameworks, however, are inherently transductive and unable to generalize to unseen nodes or learn representations across different graphs. Although inductive approaches can generalize to unseen nodes, they neglect different contexts of nodes and cannot learn node embeddings dually. In this paper, we present a context-aware unsupervised dual encoding framework, CADE, to generate representations of nodes by combining real-time neighborhoods with neighbor-attentioned representation, and preserving extra memory of known nodes. We exhibit that our approach is effective by comparing to state-of-the-art methods.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/07/2017

Inductive Representation Learning on Large Graphs

Low-dimensional embeddings of nodes in large graphs have proved extremel...
research
11/08/2021

Inferential SIR-GN: Scalable Graph Representation Learning

Graph representation learning methods generate numerical vector represen...
research
02/17/2023

Creating generalizable downstream graph models with random projections

We investigate graph representation learning approaches that enable mode...
research
05/20/2018

Learning Graph-Level Representations with Gated Recurrent Neural Networks

Recently a variety of methods have been developed to encode graphs into ...
research
08/21/2019

Hebbian Graph Embeddings

Representation learning has recently been successfully used to create ve...
research
06/01/2023

Graph-Level Embedding for Time-Evolving Graphs

Graph representation learning (also known as network embedding) has been...
research
06/07/2021

Adversarially Regularized Graph Attention Networks for Inductive Learning on Partially Labeled Graphs

Graph embedding is a general approach to tackling graph-analytic problem...

Please sign up or login with your details

Forgot password? Click here to reset