MetaSieve: Faster Relational Deep Learning through SQL-Based Metapath Selection

arXiv:2608.25903 · cs.DB, cs.LG · Submitted 2026-08-26 · Read on arXiv

cs.DB, cs.LG

Submitted: 2026-08-26

Updated: 2026-08-26

Code: https://github.com/lids-lab/metasieve

Terminology

Pruning — 44× in this paper · explained in 4 episodes
Accuracy — 29× in this paper · explained in 1 episode
Transformer — 11× in this paper · explained in 2 episodes
Aggregation — 9× in this paper · explained in 1 episode
Benchmark — 9× in this paper · explained in 2 episodes
Heterogeneous Graph — 8× in this paper · explained in 2 episodes
Relational Deep Learning — 8× in this paper
Ablation — 7× in this paper · explained in 1 episode
Foundation Model — 5× in this paper · explained in 3 episodes
Clustering — 4× in this paper · explained in 2 episodes
Feature Selection — 4× in this paper · explained in 4 episodes
Representation Learning — 4× in this paper · explained in 2 episodes
Foundation Models — 3× in this paper · explained in 7 episodes
GNNs — 3× in this paper · explained in 2 episodes
Ablation Studies — 2× in this paper · explained in 1 episode
Ablation study — 2× in this paper · explained in 1 episode
Core — 2× in this paper · explained in 2 episodes
Graph Neural Network (GNN) — 2× in this paper · explained in 2 episodes
Predictive Modeling — 2× in this paper · explained in 2 episodes
Adam — 1× in this paper · explained in 2 episodes
Alignment — 1× in this paper · explained in 1 episode
Benchmarking — 1× in this paper · explained in 3 episodes
Consistency — 1× in this paper · explained in 3 episodes
Graph Construction — 1× in this paper · explained in 3 episodes
Heterogeneous Graph Transformer — 1× in this paper
In-Context Learning — 1× in this paper · explained in 4 episodes
Model-Agnostic — 1× in this paper · explained in 2 episodes
Temporal consistency — 1× in this paper · explained in 2 episodes

Terminology

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