Graph Distribution-valued Signals: A Wasserstein Space Perspective

• URL: http://arxiv.org/abs/2509.25802v1
• Date: Tue, 30 Sep 2025 05:21:18 GMT
• Title: Graph Distribution-valued Signals: A Wasserstein Space Perspective
• Authors: Yanan Zhao, Feng Ji, Xingchao Jian, Wee Peng Tay,
• Abstract summary: We introduce a novel framework for graph signal processing (GSP) that models signals as graph distribution-valued signals (GDSs)<n>By representing signals as distributions, GDSs naturally encode uncertainty andity, while strictly generalizing traditional graph signals.<n>We establish a systematic dictionary mapping core GSP concepts to their GDS counterparts, demonstrating that classical definitions are recovered as special cases.
• Score: 34.19538134374583
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a novel framework for graph signal processing (GSP) that models signals as graph distribution-valued signals (GDSs), which are probability distributions in the Wasserstein space. This approach overcomes key limitations of classical vector-based GSP, including the assumption of synchronous observations over vertices, the inability to capture uncertainty, and the requirement for strict correspondence in graph filtering. By representing signals as distributions, GDSs naturally encode uncertainty and stochasticity, while strictly generalizing traditional graph signals. We establish a systematic dictionary mapping core GSP concepts to their GDS counterparts, demonstrating that classical definitions are recovered as special cases. The effectiveness of the framework is validated through graph filter learning for prediction tasks, supported by experimental results.

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