Nonparametric estimation of time-varying network connections by multi-stage smoothing

• URL: http://arxiv.org/abs/2605.06862v1
• Date: Thu, 07 May 2026 19:05:14 GMT
• Title: Nonparametric estimation of time-varying network connections by multi-stage smoothing
• Abstract summary: We consider the problem of estimating the underlying edge probabilities of a time-varying network observed at multiple time points.<n>We propose a multi-stage smoothing estimator that first applies temporal local smoothing to each edge and then performs node-domain smoothing.
• Score: 7.678575259856196
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider the problem of estimating the underlying edge probabilities of a time-varying network observed at multiple time points. The probability structure is represented by a time-varying graphon that satisfies temporal Hölder smoothness and piecewise Lipschitz conditions in the latent variables. We propose a multi-stage smoothing estimator that first applies temporal local smoothing to each edge and then performs node-domain smoothing using a data-driven neighborhood construction adapted from the method. An additional temporal smoothing step is introduced as an optional refinement when uniform accuracy over the entire time domain is required. Simulation studies demonstrate the benefits of combining temporal and node-domain smoothing under different generative models. We also apply the method to a real time-varying network dataset and show that it captures both smooth temporal evolution and structural patterns in the connectivity.

Related papers

• TiWeaver: Unified Temporal Dynamics Modeling via Contextual Patching [15.279995924017458]
  TiWeaver is a unified framework designed to handle temporal dynamics and fine-grained inter-channel dependencies adaptively.<n>We evaluate TiWeaver on 12 real-world time series datasets, where it achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-06-02T04:06:12Z)
• Whittaker-Henderson smoother for long satellite image time series interpolation [0.27074235008521236]
  Whittaker smoother is a widely adopted solution to pre-process satellite image time series.<n>This paper addresses two key limitations by casting Whittaker smoother as a differentiable neural layer.<n>The framework is further extended to handle heteroscedastic noise through a time-varying regularization.
  arXiv  Detail & Related papers  (2026-03-30T11:04:48Z)
• Forecasting as Rendering: A 2D Gaussian Splatting Framework for Time Series Forecasting [79.37674445572462]
  Time series forecasting (TSF) remains a challenging problem due to the intricate entanglement of intraperiod-fluctuations and interperiod-trends.<n>Treating reshaped tensors as static images results in a topological mismatch.<n> relying on uniform fixed-size representations allocates modeling capacity inefficiently.<n>TimeGS is a novel framework that fundamentally shifts the forecasting paradigm from regression to 2D generative rendering.
  arXiv  Detail & Related papers  (2026-02-10T14:13:36Z)
• Adaptive Fuzzy Time Series Forecasting via Partially Asymmetric Convolution and Sub-Sliding Window Fusion [0.0]
  We propose a novel convolutional architecture with partially asymmetric design based on the time of sliding window.<n>The proposed method achieves state-of-the-art results on most of popular time series datasets.
  arXiv  Detail & Related papers  (2025-07-28T08:58:25Z)
• TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling [67.02157180089573]
  Time series pre-training has recently garnered wide attention for its potential to reduce labeling expenses and benefit various downstream tasks. This paper proposes TimeSiam as a simple but effective self-supervised pre-training framework for Time series based on Siamese networks.
  arXiv  Detail & Related papers  (2024-02-04T13:10:51Z)
• Intensity Profile Projection: A Framework for Continuous-Time Representation Learning for Dynamic Networks [50.2033914945157]
  We present a representation learning framework, Intensity Profile Projection, for continuous-time dynamic network data. The framework consists of three stages: estimating pairwise intensity functions, learning a projection which minimises a notion of intensity reconstruction error. Moreoever, we develop estimation theory providing tight control on the error of any estimated trajectory, indicating that the representations could even be used in quite noise-sensitive follow-on analyses.
  arXiv  Detail & Related papers  (2023-06-09T15:38:25Z)
• Irregularly-Sampled Time Series Modeling with Spline Networks [0.0]
  We propose using the splines as an input to a neural network, in particular, applying the transformations on the interpolating function directly. This allows us to represent the irregular sequence compactly and use this representation in the downstream tasks such as classification and forecasting.
  arXiv  Detail & Related papers  (2022-10-19T15:05:41Z)
• Direct Embedding of Temporal Network Edges via Time-Decayed Line Graphs [51.51417735550026]
  Methods for machine learning on temporal networks generally exhibit at least one of two limitations. We present a simple method that avoids both shortcomings: construct the line graph of the network, which includes a node for each interaction, and weigh the edges of this graph based on the difference in time between interactions. Empirical results on real-world networks demonstrate our method's efficacy and efficiency on both edge classification and temporal link prediction.
  arXiv  Detail & Related papers  (2022-09-30T18:24:13Z)
• Temporally-Consistent Surface Reconstruction using Metrically-Consistent Atlases [131.50372468579067]
  We propose a method for unsupervised reconstruction of a temporally-consistent sequence of surfaces from a sequence of time-evolving point clouds. We represent the reconstructed surfaces as atlases computed by a neural network, which enables us to establish correspondences between frames. Our approach outperforms state-of-the-art ones on several challenging datasets.
  arXiv  Detail & Related papers  (2021-11-12T17:48:25Z)
• Spatio-Temporal Variational Gaussian Processes [26.60276485130467]
  We introduce a scalable approach to Gaussian process inference that combinestemporal-temporal filtering with natural variational inference. We derive a sparse approximation that constructs a state-space model over a reduced set of inducing points. We show that for separable Markov kernels the full sparse cases recover exactly the standard variational GP.
  arXiv  Detail & Related papers  (2021-11-02T16:53:31Z)
• Evolutionary Robust Clustering Over Time for Temporal Data [19.246970766964033]
  In many clustering scenes, data samples' attribute values change over time. This paper proposes a new clustering framework called evolutionary robust clustering over time. One significant innovation of the proposed framework is processing the temporal smoothness in an a posteriori manner.
  arXiv  Detail & Related papers  (2021-06-14T09:07:42Z)
• Supporting Optimal Phase Space Reconstructions Using Neural Network Architecture for Time Series Modeling [68.8204255655161]
  We propose an artificial neural network with a mechanism to implicitly learn the phase spaces properties. Our approach is either as competitive as or better than most state-of-the-art strategies.
  arXiv  Detail & Related papers  (2020-06-19T21:04:47Z)

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.