A Distributed Iteration Algorithm for Learning Time-Varying Graph Model

Mou Ma

doi:10.1007/s00034-025-03373-6

A Distributed Iteration Algorithm for Learning Time-Varying Graph Model

2025-10-01

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DOI：10.1007/s00034-025-03373-6

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In this paper, we address the problem of learning time-varying graph models from spatiotemporal signals by jointly capturing spatial and temporal dependencies. Existing graph learning methods are predominantly centralized, resulting in high computational costs for large-scale data, while distributed methods that consider only spatial correlations face limitations in handling dynamic signals. To overcome these challenges, a novel distributed graph learning algorithm is proposed, which leverages temporal correlations to efficiently learn evolving graph structures. Experimental results on synthetic and real-world datasets demonstrate that the proposed algorithm achieves competitive accuracy compared to centralized methods and outperforms existing distributed approaches.

Keywords:

Time-varying graph signals

Correlation

Distributed graph learning algorithm

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A Distributed Iteration Algorithm for Learning Time-Varying Graph Model

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