"Graph Generative Pre-trained Transformer"

Transforms graphs into sequences for better generation, like how LLMs handle text.

G2PT introduces a novel way to represent graphs as sequences, making graph generation more efficient and adaptable using transformer architecture.

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https://arxiv.org/abs/2501.01073

🤔 Original Problem:

→ Current graph generation models use complex adjacency matrices, making them computationally expensive and less efficient for sparse graphs.

→ Existing methods struggle with scaling and often require many denoising steps.

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🔧 Solution in this Paper:

→ G2PT represents graphs as sequences of node and edge definitions instead of matrices.

→ First defines all nodes with their types and indices.

→ Then specifies edges using the defined node indices and edge labels.

→ Uses transformer decoder to predict next tokens in the sequence.

→ Implements fine-tuning strategies for goal-oriented generation and property prediction.

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💡 Key Insights:

→ Sequence representation is more memory-efficient than adjacency matrices

→ Auto-regressive approach provides better control over graph generation

→ Model scales effectively with increasing data and parameters

→ Fine-tuning enables adaptation for specific tasks

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📊 Results:

→ Outperforms state-of-the-art on 11/24 metrics in generic graph generation

→ Achieves 96.4% validity on molecular generation tasks

→ Shows strong performance in goal-oriented generation

→ Matches top models in property prediction with 73.3% average ROC-AUC

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