SPP-CNN: An Efficient Framework for Network Robustness Prediction

Authors：Wu, Chengpei; Lou, Yang^*; Wang, Lin; Li, Junli^*; Li, Xiang; Chen, Guanrong

Source：IEEE Transactions on Circuits and Systems I: Regular Papers , 2023, 70(10): 4067-4079.

DOI：10.1109/TCSI.2023.3296602

• Index Terms- Complex network
• robustness
• convolutional neural network
• spatial pyramid pooling
• prediction

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.

• Institution
  y; 上海交通大学; 同济大学