Representation Learning on Graphs: Methods and Applications
Abstract
Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks. The primary challenge in this domain is finding a way to represent, or encode, graph structure so that it can be easily exploited by machine learning models. Traditionally, machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a...
Paper Details
Title
Representation Learning on Graphs: Methods and Applications
Published Date
Sep 17, 2017
Journal
Volume
40
Pages
52 - 74
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