Time-Varying Graph Learning with Constraints on Graph Temporal Variation — Paper

Abstract

-cross Abstract: We propose a novel framework for learning time-varying graphs from spatiotemporal measurements. Given an appropriate prior on the temporal behavior of signals, our proposed method can estimate time-varying graphs from a small number of available measurements. To achieve this, we introduce three regularization terms in convex optimization problems that constrain the sparseness of temporal variations of the time-varying networks. Moreover, a computationally scalable algorithm is introduced to solve the optimization problem efficiently. The experimental results with synthetic and real datasets (point cloud, temperature, and EEG data) demonstrate that our proposed method outperforms state-of-the-art methods.

Authors 4

Haruki Yokota, Koki Yamada, Yuichi Tanaka, Antonio Ortega

Specification

Official page: arxiv.org/abs/2001.03346
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Arxiv announce type: replace
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
arXiv id: 2001.03346
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Categories: eess.SP, cs.LG
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
DOI: 10.1109/TSP.2026.3712068
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
PDF: arxiv.org/pdf/2001.03346
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Primary category: eess.SP
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 2 h agohigh

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Attributed facts

10

Source tiers

T110

Freshest observation

2 h ago

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Authors: Haruki Yokota, Koki Yamada, Yuichi Tanaka

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Claim history for PDF
Value	Valid from → to	Status	Source	Confidence	Extractor
https://arxiv.org/pdf/2001.03346	11 Sept 2026 21:02 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
New paperPaperTime-Varying Graph Learning with Constraints on Graph Temporal Variation
New paper: Time-Varying Graph Learning with Constraints on Graph Temporal Variation
arxiv
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Source documents
Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.LG	feed	T1· Official	40 min ago	1

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