Graph-Aware Isomorphic Attention for Adaptive Dynamics in Transformers
We present an approach to modifying Transformer architectures by integrating
graph-aware relational reasoning into the attention mechanism, merging concepts
from graph neural networks and language modeling. Building on the inherent
connection between attention and graph theory, we reformulate the Transformer's
attention mechanism as a graph operation and propose Graph-Aware Isomorphic
Attention. This method leverages advanced graph modeling strategies, including
Graph Isomorphism Networks (GIN) and Principal Neighborhood Aggregation (PNA),
to enrich the representation of relational structures. Our approach captures
complex dependencies and generalizes across tasks, as evidenced by a reduced
generalization gap and improved learning performance. Additionally, we expand
the concept of graph-aware attention to introduce Sparse GIN-Attention, a
fine-tuning approach that employs sparse GINs. By interpreting attention
matrices as sparse adjacency graphs, this technique enhances the adaptability
of pre-trained foundational models with minimal computational overhead,
endowing them with graph-aware capabilities. Sparse GIN-Attention fine-tuning
achieves improved training dynamics and better generalization compared to
alternative methods like low-rank adaption (LoRA). We discuss latent graph-like
structures within traditional attention mechanisms, offering a new lens through
which Transformers can be understood. By evolving Transformers as hierarchical
GIN models for relational reasoning. This perspective suggests profound
implications for foundational model development, enabling the design of
architectures that dynamically adapt to both local and global dependencies.
Applications in bioinformatics, materials science, language modeling, and
beyond could benefit from this synthesis of relational and sequential data
modeling, setting the stage for interpretable and generalizable modeling
strategies.
677cfd73f7545101cef8b48b
Markus J. Buehler
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2025-01-13T21:05:51.439Z
623ce1c6b66fedf374859fe7
https://cdn-avatars.huggingface.co/v1/production/uploads/623ce1c6b66fedf374859fe7/lhbMLg6BxLCb9DD4rgjfx.jpeg
Markus Buehler
user
mjbuehler
Graph-Aware Isomorphic Attention for Adaptive Dynamics in Transformers
We present an approach to modifying Transformer architectures by integrating
graph-aware relational reasoning into the attention mechanism, merging concepts
from graph neural networks and language modeling. Building on the inherent
connection between attention and graph theory, we reformulate the Transformer's
attention mechanism as a graph operation and propose Graph-Aware Isomorphic
Attention. This method leverages advanced graph modeling strategies, including
Graph Isomorphism Networks (GIN) and Principal Neighborhood Aggregation (PNA),
to enrich the representation of relational structures. Our approach captures
complex dependencies and generalizes across tasks, as evidenced by a reduced
generalization gap and improved learning performance. Additionally, we expand
the concept of graph-aware attention to introduce Sparse GIN-Attention, a
fine-tuning approach that employs sparse GINs. By interpreting attention
matrices as sparse adjacency graphs, this technique enhances the adaptability
of pre-trained foundational models with minimal computational overhead,
endowing them with graph-aware capabilities. Sparse GIN-Attention fine-tuning
achieves improved training dynamics and better generalization compared to
alternative methods like low-rank adaption (LoRA). We discuss latent graph-like
structures within traditional attention mechanisms, offering a new lens through
which Transformers can be understood. By evolving Transformers as hierarchical
GIN models for relational reasoning. This perspective suggests profound
implications for foundational model development, enabling the design of
architectures that dynamically adapt to both local and global dependencies.
Applications in bioinformatics, materials science, language modeling, and
beyond could benefit from this synthesis of relational and sequential data
modeling, setting the stage for interpretable and generalizable modeling
strategies.
The paper proposes Graph-Aware Isomorphic Attention and Sparse GIN-Attention to integrate graph reasoning into Transformers, improving relational understanding and performance in various applications.
Transformer architectures graph-aware relational reasoning attention mechanism graph neural networks Graph Isomorphism Networks (GIN) Principal Neighborhood Aggregation (PNA) Graph-Aware Isomorphic Attention Sparse GIN-Attention attention matrices sparse adjacency graphs low-rank adaption (LoRA) hierarchical GIN models