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A multi-semantic basis representation learning paradigm that models each multi-label node as an adaptive composition of semantic bases, thereby enabling flexible representational capacity for modeling multiple semantics.
CHARM is proposed, a multimodal graph foundation model with hierarchical context modeling for zero-shot transfer that replaces isolated raw nodes with hierarchical graph contexts that capture multimodal semantics and cross-modal relations.
SliGFM is proposed, a graph foundation model built upon topology-aware sliding-window feature encoding and generative reconstruction that enables a smoothness-aware transformer to capture transferable relational patterns among feature tokens within each node, while the generative reconstruction objective encourages pre...
A Propagation-aware Graph Foundation Model (ProGFM), which regards the propagation relationships between edges and feature dimensions as transferable knowledge units, and exhibits superior generalization performance compared with existing methods.
HGMRP extends single-relation prompts into a composite structure that includes both relation-specific and shared components, thereby enhancing expressive capability and significantly out-performs existing heterogeneous prompt methods, validating its effectiveness and superiority.
This work proposes AgentGFM, in which all node agents follow a shared end-to-end trainable policy rather than using independent models, and describes this capability as information-flow control, which is inspired by recent advances in agent technology.
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