Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.22384 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911717495144448 |
|---|---|
| author | Li, Ziming Wu, Xiaoming Wang, Zehong Li, Jiazheng Tian, Yijun Bi, Jinhe Ma, Yunpu Ye, Yanfang Zhang, Chuxu |
| author_facet | Li, Ziming Wu, Xiaoming Wang, Zehong Li, Jiazheng Tian, Yijun Bi, Jinhe Ma, Yunpu Ye, Yanfang Zhang, Chuxu |
| contents | 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. The codebase, model, and datasets are available at https://github.com/zmli6/G-Substrate. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_22384 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Graph is a Substrate Across Data Modalities Li, Ziming Wu, Xiaoming Wang, Zehong Li, Jiazheng Tian, Yijun Bi, Jinhe Ma, Yunpu Ye, Yanfang Zhang, Chuxu Machine Learning Artificial Intelligence 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. The codebase, model, and datasets are available at https://github.com/zmli6/G-Substrate. |
| title | Graph is a Substrate Across Data Modalities |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2601.22384 |