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2020 | OriginalPaper | Chapter

10. Dynamic Graph Embedding

Authors : Sujit Rokka Chhetri, Mohammad Abdullah Al Faruque

Published in: Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis

Publisher: Springer International Publishing

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Abstract

In Chap. 9, we presented a structural graph convolutional neural network which is capable of performing supervising learning to estimate a function between non-euclidean data and categorical data. In this chapter, we focus on non-euclidean data which are evolving over time. In the cyber-physical system, most of the non-euclidean data (such as engineering data, energy, and signal flow graph, call graph of the firmware, etc.) are always evolving. Hence, it is necessary to utilize algorithms that are capable of handling such temporally evolving non-euclidean data. In this chapter, we present a novel dynamic graph embedding algorithm to handle this issue. In the rest of the chapter, we consider temporally evolving graphs as the non-euclidean data and present an algorithm capable of capturing the pattern of time-varying links.

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previous chapter Non-euclidean Data-Driven Modeling Using Graph Convolutional Neural Networks
Footnotes
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Metadata
Title
Dynamic Graph Embedding
Authors
Sujit Rokka Chhetri
Mohammad Abdullah Al Faruque
Copyright Year
2020
Book
Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis

Print ISBN: 978-3-030-37961-2

Electronic ISBN: 978-3-030-37962-9

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-37962-9_10