Network Recovery from Cascade Data: A Debiased Jacobian-Based Machine Learning Approach

• URL: http://arxiv.org/abs/2606.07483v1
• Date: Fri, 05 Jun 2026 17:38:05 GMT
• Title: Network Recovery from Cascade Data: A Debiased Jacobian-Based Machine Learning Approach
• Abstract summary: We propose CascadeNet, a Jacobian-based machine learning framework for network recovery.<n>Key idea is that the underlying influence structure can be characterized by the Jacobian of the one-step transition function.<n>We validate CascadeNet in both a simulation exercise and a real-world empirical application.
• Score: 6.9585728535923055
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Many important outcomes unfold as dynamic cascades, including product adoption, disease spread, financial distress, and information diffusion. A central challenge is to recover the hidden influence network behind these cascades. Existing methods typically assume a specific diffusion model, and their performance degrades substantially when that assumption is misspecified. We propose CascadeNet, a Jacobian-based machine learning framework for network recovery that does not require specifying a diffusion mechanism. The key idea is that the underlying influence structure can be characterized by the Jacobian of the one-step transition function. CascadeNet first constructs a flexible estimator of the transition function, and further applies Neyman-orthogonal debiasing via the Riesz representer, so that the debiased Jacobian is $\sqrt{n}$-consistent and asymptotically normal, enabling formal inference on the network structure. We validate CascadeNet in both a simulation exercise and a real-world empirical application. In simulations, where the data-generating process is known, CascadeNet achieves the highest network recovery accuracy across nine common data-generating processes. In an empirical application to COVID-19 transmission across Spain's 52 provinces, CascadeNet recovers transmission networks that are significantly correlated with the true inter-province mobility network, whereas networks recovered by baseline methods show no significant alignment with the ground truth.

Related papers

• Adversarial generalization of unfolding (model-based) networks [0.0]
  We study the adversarial generalization of unfolding networks when perturbed with $l$-norm constrained attacks.<n>This is the first theoretical analysis on the adversarial generalization of unfolding networks.<n>We present a series of experiments on real-world data, with results corroborating our derived theory.
  arXiv  Detail & Related papers  (2025-09-18T19:17:07Z)
• Predicting Cascading Failures with a Hyperparametric Diffusion Model [66.89499978864741]
  We study cascading failures in power grids through the lens of diffusion models. Our model integrates viral diffusion principles with physics-based concepts. We show that this diffusion model can be learned from traces of cascading failures.
  arXiv  Detail & Related papers  (2024-06-12T02:34:24Z)
• Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust Closed-Loop Control [63.310780486820796]
  We show how a parameterization of recurrent connectivity influences robustness in closed-loop settings. We find that closed-form continuous-time neural networks (CfCs) with fewer parameters can outperform their full-rank, fully-connected counterparts.
  arXiv  Detail & Related papers  (2023-10-05T21:44:18Z)
• Equilibrated Zeroth-Order Unrolled Deep Networks for Accelerated MRI [14.586911990418624]
  Recently, model-driven deep learning unrolls a certain iterative algorithm of a regularization model into a cascade network. In theory, there is not necessarily such a functional regularizer whose first-order information matches the replaced network module. This paper propose to present a safeguarded methodology on network unrolling.
  arXiv  Detail & Related papers  (2021-12-18T09:47:19Z)
• Clustering-Based Interpretation of Deep ReLU Network [17.234442722611803]
  We recognize that the non-linear behavior of the ReLU function gives rise to a natural clustering. We propose a method to increase the level of interpretability of a fully connected feedforward ReLU neural network.
  arXiv  Detail & Related papers  (2021-10-13T09:24:11Z)
• Mitigating Performance Saturation in Neural Marked Point Processes: Architectures and Loss Functions [50.674773358075015]
  We propose a simple graph-based network structure called GCHP, which utilizes only graph convolutional layers. We show that GCHP can significantly reduce training time and the likelihood ratio loss with interarrival time probability assumptions can greatly improve the model performance.
  arXiv  Detail & Related papers  (2021-07-07T16:59:14Z)
• Deep Archimedean Copulas [98.96141706464425]
  ACNet is a novel differentiable neural network architecture that enforces structural properties. We show that ACNet is able to both approximate common Archimedean Copulas and generate new copulas which may provide better fits to data.
  arXiv  Detail & Related papers  (2020-12-05T22:58:37Z)
• Joint Inference of Diffusion and Structure in Partially Observed Social Networks Using Coupled Matrix Factorization [3.399624105745357]
  In this paper, a model is learned from partially observed data to infer unobserved diffusion and structure networks. The interrelations among links of nodes and cascade processes are utilized in the proposed method via learning and low-dimensional latent factors. Experiments on these synthetic and real-world datasets show that the proposed method successfully detects invisible social behaviors, predicts links, and identifies latent features.
  arXiv  Detail & Related papers  (2020-10-03T17:48:57Z)
• 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 Collaborative Embedding for information cascade prediction [58.90540495232209]
  We propose a novel model called Deep Collaborative Embedding (DCE) for information cascade prediction. We propose an auto-encoder based collaborative embedding framework to learn the node embeddings with cascade collaboration and node collaboration. The results of extensive experiments conducted on real-world datasets verify the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-01-18T13:32:18Z)

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.