Graphbind uses topology to unify multimodal graph data representations
Toward Omni Multimodal Graph Foundation Model: A Topology-Driven Binding Approach
Machine Learning
Summary
Graphs that mix different types of information often miss some parts, making it hard for computers to learn from them. The authors propose GraphBind, a method that uses the graph's structure to connect and combine all the different data types into one shared understanding. This helps computers do better at recognizing patterns and creating new data, improving performance on many tests compared to existing methods. The key idea is that the shape of the graph helps organize information and guide how different data types relate to each other.
What this means in practice
- •For data engineers: Improve data integration pipelines by unifying incomplete multimodal graph data using topology-driven binding for more robust feature representations.
- •For recommendation system developers: Enhance recommendation algorithms by robustly combining multimodal graph attributes with structural context for better user-item relationship modeling.
Authors
Xunkai Li, Chenxi Wan, Yinlin Zhu, Wang Luo, Hongchao Qin, Rong-Hua Li, Guoren Wang
Abstract
Multimodal graph foundation models (MGFMs) seek to learn generalizable representations from large-scale graphs with heterogeneous node modalities. However, real-world Multimodal-Attributed Graphs (MAGs) often contain incomplete node attributes, limiting the scale and diversity of available pretraining corpora. Besides, existing MGFMs primarily incorporate graph topology as structural context, overlooking its role in guiding multimodal binding and shaping a unified representation space. To address these challenges, we propose GraphBind, a topology-driven approach that uses graph topology to bind rich modality information into a unified shared space. GraphBind is motivated by the stability of graph topology, which provides structural references and complementary semantic information for multimodal binding. Concretely, GraphBind uses topology to organize self semantics and reliable neighborhood semantics into a global shared space that integrates structure and semantics, and adapts this space to discriminative and generative tasks through lightweight interfaces. Extensive experiments against 11 representative baselines demonstrate that GraphBind achieves leading performance on both discriminative and generative tasks, with relative improvements of up to 28.1% over the strongest baseline.