Graph is a Substrate Across Data Modalities

• URL: http://arxiv.org/abs/2601.22384v1
• Date: Thu, 29 Jan 2026 22:46:02 GMT
• Title: Graph is a Substrate Across Data Modalities
• Authors: Ziming Li, Xiaoming Wu, Zehong Wang, Jiazheng Li, Yijun Tian, Jinhe Bi, Yunpu Ma, Yanfang Ye, Chuxu Zhang,
• Abstract summary: Graphs provide a natural representation of relational structure that arises across diverse domains.<n>Despite this ubiquity, graph structure is typically learned in a modality- and task-isolated manner.<n>We propose G-Substrate, a graph substrate framework that organizes learning around shared graph structures.
• Score: 50.51414622183521
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graphs provide a natural representation of relational structure that arises across diverse domains. Despite this ubiquity, graph structure is typically learned in a modality- and task-isolated manner, where graph representations are constructed within individual task contexts and discarded thereafter. As a result, structural regularities across modalities and tasks are repeatedly reconstructed rather than accumulated at the level of intermediate graph representations. This motivates a representation-learning question: how should graph structure be organized so that it can persist and accumulate across heterogeneous modalities and tasks? We adopt a representation-centric perspective in which graph structure is treated as a structural substrate that persists across learning contexts. To instantiate this perspective, we propose G-Substrate, a graph substrate framework that organizes learning around shared graph structures. G-Substrate comprises two complementary mechanisms: a unified structural schema that ensures compatibility among graph representations across heterogeneous modalities and tasks, and an interleaved role-based training strategy that exposes the same graph structure to multiple functional roles during learning. Experiments across multiple domains, modalities, and tasks show that G-Substrate outperforms task-isolated and naive multi-task learning methods.

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