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ThinkMind // International Journal On Advances in Networks and Services, volume 13, numbers 3 and 4, 2020 // View article netser_v13_n34_2020_3


Teaching Machines to Understand Urban Networks: A Graph Autoencoder Approach

Authors:
Maria Coelho
Mark Austin
Shivam Mishra
Mark Blackburn

Keywords: Systems Engineering; Machine Learning; Graph Embeddings; Graph Autoencoders; Digital Twins; Water Distribution Systems.

Abstract:
Due to remarkable advances in computer, communications and sensing technologies over the past three decades, large-scale urban systems are now far more heterogeneous and automated than their predecessors. They may, in fact, be connected to other types of systems in completely new ways. These characteristics make the tasks of system design, analysis and integration of multi-disciplinary concerns much more difficult than in the past. We believe these challenges can be addressed by teaching machines to understand urban networks. This paper explores opportunities for using a recently developed graph autoencoding approach to encode the structure and associated network attributes as low-dimensional vectors. We exercise the proposed approach on a problem involving identification of leaks in urban water distribution systems.

Pages: 70 to 81

Copyright: Copyright (c) to authors, 2020. Used with permission.

Publication date: December 30, 2020

Published in: journal

ISSN: 1942-2644

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