SPP-CNN: An Efficient Framework for Network Robustness Prediction

• Published in: arXiv.org 2023-05
• Main Authors: Wu, Chengpei, Yang, Lou, Wang, Lin, Li, Junli, Li, Xiang, Chen, Guanrong
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Controllability; Graph neural networks; Neural networks; Robustness
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2305.07872

This paper addresses the robustness of a network to sustain its connectivity and controllability against malicious attacks. This kind of network robustness is typically measured by the time-consuming attack simulation, which returns a sequence of values that record the remaining connectivity and controllability after a sequence of node- or edge-removal attacks. For improvement, this paper develops an efficient framework for network robustness prediction, the spatial pyramid pooling convolutional neural network (SPP-CNN). The new framework installs a spatial pyramid pooling layer between the convolutional and fully-connected layers, overcoming the common mismatch issue in the CNN-based prediction approaches and extending its generalizability. Extensive experiments are carried out by comparing SPP-CNN with three state-of-the-art robustness predictors, namely a CNN-based and two graph neural networks-based frameworks. Synthetic and real-world networks, both directed and undirected, are investigated. Experimental results demonstrate that the proposed SPP-CNN achieves better prediction performances and better generalizability to unknown datasets, with significantly lower time-consumption, than its counterparts.