CNN-based Prediction of Network Robustness With Missing Edges

Chengpei WU, Yang LOU, Ruizi WU, Wenwen LIU, Junli LI

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Referred Conference Paperpeer-review

Abstract

Connectivity and controllability of a complex network are two important issues that guarantee a networked system to function. Robustness of connectivity and controllability guarantees the system to function properly and stably under various malicious attacks. Evaluating network robustness using attack simulations is time consuming, while the convolutional neural network (CNN)-based prediction approach provides a cost-efficient method to approximate the network robustness. In this paper, we investigate the performance of CNN-based approaches for connectivity and controllability robustness prediction, when partial network information is missing, namely the adjacency matrix is incomplete. Extensive experimental studies are carried out. A threshold is explored that if a total amount of more than 7.29% information is lost, the performance of CNN-based prediction will be significantly degenerated for all cases in the experiments. Two scenarios of missing edge representations are compared, 1) a missing edge is marked ‘no edge’ in the input for prediction, and 2) a missing edge is denoted using a special marker of ‘unknown’. Experimental results reveal that the first representation is misleading to the CNN-based predictors.
Original languageEnglish
Title of host publication2022 International Joint Conference on Neural Networks (IJCNN)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-8
ISBN (Electronic)9781728186719
DOIs
Publication statusE-pub ahead of print - 30 Sep 2022
Event2022 International Joint Conference on Neural Networks (IJCNN) - Padua, Italy
Duration: 18 Jul 202223 Jul 2022

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2022-July

Conference

Conference2022 International Joint Conference on Neural Networks (IJCNN)
Period18/07/2223/07/22

Bibliographical note

Funding Information:
This research was supported by the National Natural Science Foundation of China (No. 62002249) and the Foundation of Key Laboratory of System Control and Information Processing, Ministry of Education, P. R. China (No. Scip202103).

Publisher Copyright:
© 2022 IEEE.

Keywords

  • Complex network
  • convolutional neural network
  • missing edge
  • prediction
  • robustness

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