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Plain Transformer model PENCIL outperforms GNNs in graph link prediction

By PulseAugur Editorial · [1 sources] ·

Researchers have developed PENCIL, a plain Transformer model that can predict links in large graphs more efficiently than traditional Graph Neural Networks (GNNs). Unlike existing Graph Transformers that require complex structural encodings, PENCIL uses attention over sampled local subgraphs. This approach allows it to capture rich topological dependencies and outperform GNNs in parameter efficiency and scalability, even without node features. AI

IMPACT Demonstrates that simpler Transformer architectures can achieve competitive performance in graph machine learning, potentially reducing the need for complex GNN engineering.

RANK_REASON Academic paper introducing a new model architecture for graph link prediction. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Plain Transformer model PENCIL outperforms GNNs in graph link prediction

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Academic paper introducing a new model architecture for graph link prediction. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Quang Truong, Yu Song, Donald Loveland, Mingxuan Ju, Tong Zhao, Neil Shah, Jiliang Tang ·

    Plain Transformers are Surprisingly Powerful Link Predictors

    arXiv:2602.01553v2 Announce Type: replace-cross Abstract: Link prediction is a core challenge in graph machine learning, demanding models that capture rich and complex topological dependencies. While Graph Neural Networks (GNNs) are the standard solution, state-of-the-art pipelin…

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