HOPPER framework enhances graph sequence models with learnable hop extraction

Researchers have introduced HOPPER, a novel framework designed to enhance Linearized Graph Sequence Models (LGSMs). Unlike previous LGSMs that rely on fixed graph operators, HOPPER enables end-to-end learning of hop sequences, allowing for adaptive propagation mechanisms tailored to specific graph structures, node features, and downstream tasks. This approach preserves permutation equivariance and has demonstrated state-of-the-art or competitive performance on benchmarks like ECHO-Synth and LRIM, particularly in handling long-range dependencies in graph representation learning. AI

IMPACT Enhances graph representation learning by enabling adaptive propagation mechanisms in sequence models.

RANK_REASON The cluster contains a research paper detailing a new model architecture. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• ECHO-Synth
• Gotit.pub
• HOPPER
• Hugging Face
• Laboratoire Recherche Informatique Maisonneuve
• Linearized Graph Sequence Models
• ScienceCast

• paper
• model release

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