SPP-CNN: An Efficient Framework for Network Robustness Prediction

• URL: http://arxiv.org/abs/2305.07872v1
• Date: Sat, 13 May 2023 09:09:20 GMT
• Title: SPP-CNN: An Efficient Framework for Network Robustness Prediction
• Authors: Chengpei Wu and Yang Lou and Lin Wang and Junli Li and Xiang Li and Guanrong Chen
• Abstract summary: 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.
• Score: 13.742495880357493
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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