A Novel Method to Enable Transfer Learning of Structural Graph Representations

Janet Layne, Edoardo Serra

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Graph Representation Learning (GRL) methods which effectively capture a node’s neighborhood structure in their representations can show excellent performance on important machine learning tasks such as node and graph classification. Recent work has focused on scaling GRL to massive graphs, but existing methods are transductive (must be re-trained for unseen nodes) and are often geared to learn proximity rather than node structure. Graph Neural Network methods can learn structure, but are often supervised, prone to learn proximity, and do not scale well for massive graphs. Transfer learning has the potential to enable scaling to massive graphs, while preventing overfitting, and creating universal models for use on a wide variety of datasets. We propose a novel method that enables transfer learning. Our model performs better at tasks which require capture of nodes’ structural information and scales as well as the current state of the art to very large graphs.

Original languageAmerican English
Title of host publication2023 IEEE International Conference on Big Data (BigData)
StatePublished - 1 Jan 2023

Keywords

  • graph representation learning
  • structural graph representations

EGS Disciplines

  • Computer Sciences

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