Improving Graph Machine Learning Performance Through Feature Augmentation Based on Network Control Theory-Reference-Cited by-同舟云学术

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Improving Graph Machine Learning Performance Through Feature Augmentation Based on Network Control Theory

Published:2024-06-11 Issue: Volume:5 Page:322-327
ISSN:
Container-title:2024 32nd Mediterranean Conference on Control and Automation (MED)
language:
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Author:

Said Anwar¹,Ahmad Obaid Ullah²,Abbas Waseem³,Shabbir Mudassir⁴,Koutsoukos Xenofon¹

Affiliation:

1. Vanderbilt University,Computer Science Department,Nashville,TN

2. University of Texas at Dallas,Electrical Engineering Department,Richardson,TX,75080

3. University of Texas at Dallas,Systems Engineering Department,Richardson,TX

4. Information Technology University,Computer Science Department,Lahore,Pakistan

Funder

National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10566119/10566120/10566241.pdf?arnumber=10566241

Reference30 articles.

1. Strong structural controllability of networks: Comparison of bounds using distances and zero forcing

2. Graph Representation Learning

3. Network Controllability Perspectives on Graph Representation

4. Contributions to the theory of optimal control;Kalman;Bol. soc. mat. mexicana,1960

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