TDNetGen: Empowering Complex Network Resilience Prediction with Generative Augmentation of Topology and Dynamics

• URL: http://arxiv.org/abs/2408.09825v1
• Date: Mon, 19 Aug 2024 09:20:31 GMT
• Title: TDNetGen: Empowering Complex Network Resilience Prediction with Generative Augmentation of Topology and Dynamics
• Authors: Chang Liu, Jingtao Ding, Yiwen Song, Yong Li,
• Abstract summary: 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%.
• Score: 14.25304439234864
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Predicting the resilience of complex networks, which represents the ability to retain fundamental functionality amidst external perturbations or internal failures, plays a critical role in understanding and improving real-world complex systems. Traditional theoretical approaches grounded in nonlinear dynamical systems rely on prior knowledge of network dynamics. On the other hand, data-driven approaches frequently encounter the challenge of insufficient labeled data, a predicament commonly observed in real-world scenarios. In this paper, we introduce a novel resilience prediction framework for complex networks, designed to tackle this issue through generative data augmentation of network topology and dynamics. The core idea is the strategic utilization of the inherent joint distribution present in unlabeled network data, facilitating the learning process of the resilience predictor by illuminating the relationship between 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%. Furthermore, the framework still demonstrates a pronounced augmentation capability in extreme low-data regimes, thereby underscoring its utility and robustness in enhancing the prediction of network resilience. We have open-sourced our code in the following link, https://github.com/tsinghua-fib-lab/TDNetGen.

Related papers

• Predicting Large-scale Urban Network Dynamics with Energy-informed Graph Neural Diffusion [51.198001060683296]
  Networked urban systems facilitate the flow of people, resources, and services.<n>Current models such as graph neural networks have shown promise but face a trade-off between efficacy and efficiency.<n>This paper addresses this trade-off by drawing inspiration from physical laws to inform essential model designs.
  arXiv  Detail & Related papers  (2025-07-31T01:24:01Z)
• Advancing network resilience theories with symbolized reinforcement learning [29.97738497697876]
  Current resilience theories address the problem from a single perspective of topology, neglecting the crucial role of system dynamics.<n>Here, we report an automatic method for resilience theory discovery, which learns from how AI solves a complicated network dismantling problem.<n>This proposed self-inductive approach discovers the first resilience theory that accounts for both topology and dynamics.
  arXiv  Detail & Related papers  (2025-07-04T19:19:35Z)
• Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning [57.3885832382455]
  We show that introducing static network sparsity alone can unlock further scaling potential beyond dense counterparts with state-of-the-art architectures.<n>Our analysis reveals that, in contrast to naively scaling up dense DRL networks, such sparse networks achieve both higher parameter efficiency for network expressivity.
  arXiv  Detail & Related papers  (2025-06-20T17:54:24Z)
• Topology-Aware Conformal Prediction for Stream Networks [54.505880918607296]
  We propose Spatio-Temporal Adaptive Conformal Inference (textttCISTA), a novel framework that integrates network topology and temporal dynamics into the conformal prediction framework. Our results show that textttCISTA effectively balances prediction efficiency and coverage, outperforming existing conformal prediction methods for stream networks.
  arXiv  Detail & Related papers  (2025-03-06T21:21:15Z)
• Coding schemes in neural networks learning classification tasks [52.22978725954347]
  We investigate fully-connected, wide neural networks learning classification tasks. We show that the networks acquire strong, data-dependent features. Surprisingly, the nature of the internal representations depends crucially on the neuronal nonlinearity.
  arXiv  Detail & Related papers  (2024-06-24T14:50:05Z)
• Disentangling the Causes of Plasticity Loss in Neural Networks [55.23250269007988]
  We show that loss of plasticity can be decomposed into multiple independent mechanisms. We show that a combination of layer normalization and weight decay is highly effective at maintaining plasticity in a variety of synthetic nonstationary learning tasks.
  arXiv  Detail & Related papers  (2024-02-29T00:02:33Z)
• Generalization and Estimation Error Bounds for Model-based Neural Networks [78.88759757988761]
  We show that the generalization abilities of model-based networks for sparse recovery outperform those of regular ReLU networks. We derive practical design rules that allow to construct model-based networks with guaranteed high generalization.
  arXiv  Detail & Related papers  (2023-04-19T16:39:44Z)
• Evaluating Distribution System Reliability with Hyperstructures Graph Convolutional Nets [74.51865676466056]
  We show how graph convolutional networks and hyperstructures representation learning framework can be employed for accurate, reliable, and computationally efficient distribution grid planning. Our numerical experiments show that the proposed Hyper-GCNNs approach yields substantial gains in computational efficiency.
  arXiv  Detail & Related papers  (2022-11-14T01:29:09Z)
• On the Application of Data-Driven Deep Neural Networks in Linear and Nonlinear Structural Dynamics [28.979990729816638]
  The use of deep neural network (DNN) models as surrogates for linear and nonlinear structural dynamical systems is explored. The focus is on the development of efficient network architectures using fully-connected, sparsely-connected, and convolutional network layers. It is shown that the proposed DNNs can be used as effective and accurate surrogates for predicting linear and nonlinear dynamical responses under harmonic loadings.
  arXiv  Detail & Related papers  (2021-11-03T13:22:19Z)
• Towards Understanding Theoretical Advantages of Complex-Reaction Networks [77.34726150561087]
  We show that a class of functions can be approximated by a complex-reaction network using the number of parameters. For empirical risk minimization, our theoretical result shows that the critical point set of complex-reaction networks is a proper subset of that of real-valued networks.
  arXiv  Detail & Related papers  (2021-08-15T10:13:49Z)
• Topological Uncertainty: Monitoring trained neural networks through persistence of activation graphs [0.9786690381850356]
  In industrial applications, data coming from an open-world setting might widely differ from the benchmark datasets on which a network was trained. We develop a method to monitor trained neural networks based on the topological properties of their activation graphs.
  arXiv  Detail & Related papers  (2021-05-07T14:16:03Z)
• Network Embedding via Deep Prediction Model [25.727377978617465]
  This paper proposes a network embedding framework to capture the transfer behaviors on structured networks via deep prediction models. A network structure embedding layer is added into conventional deep prediction models, including Long Short-Term Memory Network and Recurrent Neural Network. Experimental studies are conducted on various datasets including social networks, citation networks, biomedical network, collaboration network and language network.
  arXiv  Detail & Related papers  (2021-04-27T16:56:00Z)
• 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)
• Deep learning of contagion dynamics on complex networks [0.0]
  We propose a complementary approach based on deep learning to build effective models of contagion dynamics on networks. By allowing simulations on arbitrary network structures, our approach makes it possible to explore the properties of the learned dynamics beyond the training data. Our results demonstrate how deep learning offers a new and complementary perspective to build effective models of contagion dynamics on networks.
  arXiv  Detail & Related papers  (2020-06-09T17:18:34Z)
• Modeling Dynamic Heterogeneous Network for Link Prediction using Hierarchical Attention with Temporal RNN [16.362525151483084]
  We propose a novel dynamic heterogeneous network embedding method, termed as DyHATR. It uses hierarchical attention to learn heterogeneous information and incorporates recurrent neural networks with temporal attention to capture evolutionary patterns. We benchmark our method on four real-world datasets for the task of link prediction.
  arXiv  Detail & Related papers  (2020-04-01T17:16:47Z)

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.