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Jumping knowledge enables local and global information integration

Jumping knowledge mechanism in GNNs enables flexible integration of local and global phylogenetic information for trait evolution.

Confidence
80%
active

Evidence Quote

“Jumping knowledge may help GNNs flexibly integrate information from both local and global phylogenetic neighborhoods.”

Relationship

Jumping knowledge mechanism enables Trait imputation

Arguments

Jumping knowledge mechanismsubject
Trait imputationobject

Connections (2)

Petabase-scale sequence alignment increases viral discoveryAssociation
Reasoning on phylogenetic modeling, trait simulation, and GNN utilityInferenceChain

Evidence

“Reference to Veličković P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y. (2017)”

(2017). Graph Attention Networks doi:10.48550/ARXIV.1710.10903 ↗

“Reference to the paper on Jumping Knowledge Networks (2018)”

(2018). Representation Learning on Graphs with Jumping Knowledge Networks doi:10.48550/ARXIV.1806.03536 ↗

“Reference entry for DOI 10.57844/arcadia-zf7s-3264”

10.57844/arcadia-zf7s-3264

“Reference to Zhang C. (2023) about learnable topological features in phylogenetic inference with GNNs.”

Zhang C. (2023). Learnable Topological Features for Phylogenetic Inference via Graph Neural Networks doi:10.48550/ARXIV.2302.08840 ↗