Graph Regression
23개 벤치마크 · 논문 155편 · 이 태스크의 논문 보기 →
Benchmarks
Peptides-struct
ZINC-500k
ZINC
Lipophilicity
PCQM4Mv2-LSC
ZINC-full
PCQM4M-LSC
ESR2
F2
KIT
PARP1
PGR
ZINC 100k
Tox21
ESOL
GlassTemp
Lipophilicity
QM9
QM9: UATOM
QM9: ZPVE
QM9: del e
QM9: mu
ZINC 10k
Most implemented
Graph Attention Networks
Semi-Supervised Classification with Graph Convolutional Networks
Inductive Representation Learning on Large Graphs
Neural Message Passing for Quantum Chemistry
How Powerful are Graph Neural Networks?
Benchmarking Graph Neural Networks
Papers
HP-JEPA: Hierarchical Partitioning for Multi-Resolution Graph Joint-Embedding Predictive Learning
Graph self-supervised learning aims to learn transferable representations from large-scale unlabeled graph data. Joint-embedding predictive architectures (JEPAs) avoid explicit negative-pair construction and raw-input re…
Graph Representation LearningSelf-Supervised LearningGraph ClassificationGraph RegressionGRAFT: Biological Graph and Hypergraph Benchmarks for Linked Gene Expression and Phenotypic Trait Prediction in Arabidopsis thaliana
Understanding which genes control which traits in an organism remains one of the central challenges in biology. Despite significant advances in data collection technology, our ability to map genes to traits is still limi…
Graph RegressionGraph LearningGeodesics of Dynamic Graphs for Regime Change Detection
Traditional change point detection in dynamic networks assumes abrupt transitions between stationary states, overlooking scenarios of continuous evolution which arise in most real-world applications, such as social netwo…
Change Point DetectionChange DetectionGraph RegressionWeisfeiler-Leman Is Incomplete on Simple Spectrum Graphs, so Canonicalize Them
Graphs with a simple spectrum admit cubic-time isomorphism testing, yet we prove that for every natural number $k$, the $k$-Weisfeiler-Leman ($k$-WL) test cannot distinguish all non-isomorphic graphs with a simple spectr…
Graph RegressionBOOST-RPF: Boosted Sequential Trees for Radial Power Flow
Accurate power flow analysis is critical for modern distribution systems, yet classical solvers face scalability issues, and current machine learning models often struggle with generalization. We introduce BOOST-RPF, a n…
Graph RegressionMolGraphBench: A Benchmark of GNN Architectures for Molecular Regression Tasks
Molecules are often represented as SMILES strings, which can be readily converted to hand-crafted descriptors or fingerprints (FP) for molecular property prediction. Research has demonstrated that SMILES can be converted…
Molecular Property PredictionTransfer LearningGraph Regression