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

• URL: http://arxiv.org/abs/2603.02265v1
• Date: Sat, 28 Feb 2026 12:34:37 GMT
• Title: High-order Knowledge Based Network Controllability Robustness Prediction: A Hypergraph Neural Network Approach
• Abstract summary: We propose a dual hypergraph attention neural network model based on high-order knowledge to accomplish robustness learning and controllability curve prediction.<n>The proposed method achieves superior performance on both synthetic and real-world networks with low computational overhead.
• Score: 8.650463285269518
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Attention Augmented GNN RNN-Attention Models for Advanced Cybersecurity Intrusion Detection [0.4369550829556577]
  We propose a novel hybrid deep learning architecture that synergistically combines Graph Neural Networks (GNNs), Recurrent Neural Networks (RNNs) and multi-head attention mechanisms.<n>Our approach effectively captures both spatial dependencies through graph structural relationships and sequential analysis of network events.<n>The integrated attention mechanism provides dual benefits of improved model interpretability and enhanced feature selection, enabling cybersecurity analysts to focus computational resources on high-impact security events.
  arXiv  Detail & Related papers  (2025-10-29T03:47:02Z)
• Power Grid Control with Graph-Based Distributed Reinforcement Learning [60.49805771047161]
  This work advances a graph-based distributed reinforcement learning framework for real-time, scalable grid management.<n>A Graph Neural Network (GNN) is employed to encode the network's topological information within the single low-level agent's observation.<n>Experiments on the Grid2Op simulation environment show the effectiveness of the approach.
  arXiv  Detail & Related papers  (2025-09-02T22:17:25Z)
• Understanding Generalization, Robustness, and Interpretability in Low-Capacity Neural Networks [0.0]
  We introduce a framework to investigate capacity, sparsity, and robustness in low-capacity networks.<n>We show that trained networks are robust to extreme magnitude pruning (up to 95% sparsity)<n>This work provides a clear, empirical demonstration of the trade-offs governing simple neural networks.
  arXiv  Detail & Related papers  (2025-07-22T06:43:03Z)
• Channel Fingerprint Construction for Massive MIMO: A Deep Conditional Generative Approach [65.47969413708344]
  We introduce the concept of CF twins and design a conditional generative diffusion model (CGDM)<n>We employ a variational inference technique to derive the evidence lower bound (ELBO) for the log-marginal distribution of the observed fine-grained CF conditioned on the coarse-grained CF.<n>We show that the proposed approach exhibits significant improvement in reconstruction performance compared to the baselines.
  arXiv  Detail & Related papers  (2025-05-12T01:36:06Z)
• TDNetGen: Empowering Complex Network Resilience Prediction with Generative Augmentation of Topology and Dynamics [14.25304439234864]
  We introduce a novel resilience prediction framework for complex networks, designed to tackle this issue through generative data augmentation of network topology and dynamics. Experiment results on three network datasets demonstrate that our proposed framework TDNetGen can achieve high prediction accuracy up to 85%-95%.
  arXiv  Detail & Related papers  (2024-08-19T09:20:31Z)
• Compositional Curvature Bounds for Deep Neural Networks [7.373617024876726]
  A key challenge that threatens the widespread use of neural networks in safety-critical applications is their vulnerability to adversarial attacks. We study the second-order behavior of continuously differentiable deep neural networks, focusing on robustness against adversarial perturbations. We introduce a novel algorithm to analytically compute provable upper bounds on the second derivative of neural networks.
  arXiv  Detail & Related papers  (2024-06-07T17:50:15Z)
• Efficient Network Representation for GNN-based Intrusion Detection [2.321323878201932]
  The last decades have seen a growth in the number of cyber-attacks with severe economic and privacy damages. We propose a novel network representation as a graph of flows that aims to provide relevant topological information for the intrusion detection task. We present a Graph Neural Network (GNN) based framework responsible for exploiting the proposed graph structure.
  arXiv  Detail & Related papers  (2023-09-11T16:10:12Z)
• Bayesian Attention Belief Networks [59.183311769616466]
  Attention-based neural networks have achieved state-of-the-art results on a wide range of tasks. This paper introduces Bayesian attention belief networks, which construct a decoder network by modeling unnormalized attention weights. We show that our method outperforms deterministic attention and state-of-the-art attention in accuracy, uncertainty estimation, generalization across domains, and adversarial attacks.
  arXiv  Detail & Related papers  (2021-06-09T17:46:22Z)
• Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning [50.24174211654775]
  We present a novel contrastive self-supervised learning framework for anomaly detection on attributed networks. Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair. A graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure.
  arXiv  Detail & Related papers  (2021-02-27T03:17:20Z)
• Learning Connectivity of Neural Networks from a Topological Perspective [80.35103711638548]
  We propose a topological perspective to represent a network into a complete graph for analysis. By assigning learnable parameters to the edges which reflect the magnitude of connections, the learning process can be performed in a differentiable manner. This learning process is compatible with existing networks and owns adaptability to larger search spaces and different tasks.
  arXiv  Detail & Related papers  (2020-08-19T04:53:31Z)
• Network Diffusions via Neural Mean-Field Dynamics [52.091487866968286]
  We propose a novel learning framework for inference and estimation problems of diffusion on networks. Our framework is derived from the Mori-Zwanzig formalism to obtain an exact evolution of the node infection probabilities. Our approach is versatile and robust to variations of the underlying diffusion network models.
  arXiv  Detail & Related papers  (2020-06-16T18:45:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.