High-order Knowledge Based Network Controllability Robustness Prediction: A Hypergraph Neural Network Approach

TL;DR

This paper introduces a hypergraph neural network model that leverages high-order structural information to predict network controllability robustness efficiently, surpassing existing methods especially for large-scale networks.

Contribution

It presents the first exploration of high-order knowledge's impact on network controllability robustness using a dual hypergraph attention neural network.

Findings

Outperforms state-of-the-art methods on synthetic and real-world networks.

Effectively captures high-order structural information for robustness prediction.

Achieves low computational overhead compared to traditional simulation-based approaches.

Abstract

In order to evaluate the invulnerability of networks against various types of attacks and provide guidance for potential performance enhancement as well as controllability maintenance, network controllability robustness (NCR) has attracted increasing attention in recent years. Traditionally, controllability robustness is determined by attack simulations, which are computationally time-consuming and only applicable to small-scale networks. Although some machine learning-based methods for predicting network controllability robustness have been proposed, they mainly focus on pairwise interactions in complex networks, and the underlying relationships between high-order structural information and controllability robustness have not been explored. In this paper, a dual hypergraph attention neural network model based on high-order knowledge (NCR-HoK) is proposed to accomplish robustness learning and controllability robustness curve prediction. Through a node feature encoder, hypergraph construction with high-order relations, and a dedicated dual hypergraph attention module, the proposed method can effectively learn three types of network information simultaneously: explicit structural information in the original graph, high-order connection information in local neighborhoods, and hidden features in the embedding space. Notably, we explore for the first time the impact of high-order knowledge on network controllability robustness. Compared with state-of-the-art methods for network robustness learning, the proposed method achieves superior performance on both synthetic and real-world networks with low computational overhead.