"Best of Both Worlds: Advantages of Hybrid Graph Sequence Models"

Graphs + Transformers = the best of both worlds 🤝

GSM (Graph Sequence Mode) bridges the gap between sequence models and graph learning through intelligent tokenization

Hybrid sequence models unlock new possibilities in graph representation learning

This paper introduces a unified framework called Graph Sequence Model (GSM) that effectively adapts sequence models like Transformers and RNNs for graph-structured data. It addresses the challenge of maintaining computational efficiency while preserving structural information in graphs through a novel three-stage approach.

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https://arxiv.org/abs/2411.15671

🔍 Original Problem:

→ Traditional graph neural networks struggle with capturing long-range dependencies and global relationships in graph data

→ Existing sequence models face challenges when applied to graphs due to their complex topology and lack of natural ordering

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🛠️ Solution in this Paper:

→ GSM (Graph Sequence Mode) framework introduces three main stages: Tokenization, Local Encoding, and Global Encoding

→ Tokenization translates graphs into sequences using either node-level or subgraph-level approaches

→ Local Encoding captures neighborhood information around each node

→ Global Encoding employs scalable sequence models to capture long-range dependencies

→ GSM++ enhances the base framework by using Hierarchical Affinity Clustering for tokenization

→ A hybrid architecture combines Transformer and SSM layers to leverage their complementary strengths

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💡 Key Insights:

→ Different sequence models show distinct strengths in specific graph tasks

→ Transformers excel at global tasks but struggle with counting

→ Recurrent models perform better at sequential tasks but need careful node ordering

→ Hybrid approaches can overcome individual limitations of different architectures

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📊 Results:

→ GSM++ outperforms baselines across diverse graph benchmarks

→ Linear scaling with graph size in computational complexity

→ Achieves state-of-the-art performance on molecular property prediction tasks