Unsupervised Hierarchical Graph Representation Learning by Mutual Information Maximization

03/18/2020
by   Fei Ding, et al.
11

Graph representation learning based on graph neural networks (GNNs) can greatly improve the performance of downstream tasks, such as node and graph classification. However, the general GNN models cannot aggregate node information in a hierarchical manner, and thus cannot effectively capture the structural features of graphs. In addition, most of the existing hierarchical graph representation learning are supervised, which are limited by the extreme cost of acquiring labeled data. To address these issues, we present an unsupervised graph representation learning method, Unsupervised Hierarchical Graph Representation UHGR, which can generate hierarchical representations of graphs. This contrastive learning technique focuses on maximizing mutual information between "local" and high-level "global" representations, which enables us to learn the node embeddings and graph embeddings without any labeled data. To demonstrate the effectiveness of the proposed method, we perform the node and graph classification using the learned node and graph embeddings. The results show that the proposed method achieves comparable results to state-of-the-art supervised methods on several benchmarks. In addition, our visualization of hierarchical representations indicates that our method can capture meaningful and interpretable clusters.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/19/2019

Heterogeneous Deep Graph Infomax

Graph representation learning is to learn universal node representations...
research
10/07/2022

Unsupervised Semantic Representation Learning of Scientific Literature Based on Graph Attention Mechanism and Maximum Mutual Information

Since most scientific literature data are unlabeled, this makes unsuperv...
research
10/21/2022

HCL: Improving Graph Representation with Hierarchical Contrastive Learning

Contrastive learning has emerged as a powerful tool for graph representa...
research
08/26/2020

Learning Robust Node Representation on Graphs

Graph neural networks (GNN), as a popular methodology for node represent...
research
02/04/2020

Graph Representation Learning via Graphical Mutual Information Maximization

The richness in the content of various information networks such as soci...
research
09/27/2018

Deep Graph Infomax

We present Deep Graph Infomax (DGI), a general approach for learning nod...
research
07/07/2020

Hierarchical and Unsupervised Graph Representation Learning with Loukas's Coarsening

We propose a novel algorithm for unsupervised graph representation learn...

Please sign up or login with your details

Forgot password? Click here to reset