Co-Author: Deborah Tylor Tylor Data Services, LLC email@example.com
Co-Author: Mirco A. Mannucci HoloMathics, LLC firstname.lastname@example.org
Co-Author: Joseph Haaga Georgia Institute of Technology email@example.com
Abstract WalkRNN, the approach described herein, leverages research in learning continuous representations for nodes in networks, layers in features captured in property graph attributes and labels, and uses Deep Learning language modeling via Recurrent Neural Networks to read the grammar of an enriched property graph. We then demonstrate translating this learned graph literacy into actionable knowledge through graph classification tasks.
Keywords Deep Learning * Graph Mining * Random Walk * Language Model * Graph Language * RNN * GraphWave
Published online at viXra