SPP-CNN: An Efficient Framework for Network Robustness Prediction

TL;DR

This paper introduces SPP-CNN, an efficient neural network framework that accurately predicts network robustness against attacks, outperforming existing methods in speed and generalizability across various network types.

Contribution

The paper proposes a novel SPP-CNN framework with a spatial pyramid pooling layer, enhancing prediction accuracy and generalizability for network robustness assessment.

Findings

SPP-CNN outperforms state-of-the-art predictors in accuracy.

SPP-CNN demonstrates lower computational time.

The framework generalizes well to unseen network datasets.

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.