Model Cheatsheet¶

GNN Cheatsheet¶

GAT	The GAT (Graph Attention Network) model, based on the “Graph Attention Networks” paper.
GCN	The GCN (Graph Convolutional Network) model, based on the “Semi-supervised Classification with Graph Convolutional Networks” paper.
HAN	The Heterogeneous Graph Attention Network (HAN) model, as introduced in the “Heterogeneous Graph Attention Network” paper.
OGC	The OGC method from the “From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited” paper.
HGT	The Heterogeneous Graph Transformer (HGT) model, as introduced in the “Heterogeneous Graph Transformer” paper.
SAGE	The SAGE model, as introduced in the “Inductive Representation Learning on Large Graphs” paper.
RECT_L	The RECT model, or more specifically its supervised part RECT-L, from the “Network Embedding with Completely-imbalanced Labels” paper.
TAPE	The TAPE method from the “Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning” paper.

FTTransformer	The FT-Transformer model introduced in the “Revisiting Deep Learning Models for Tabular Data” paper.
TabTransformer	The Tab-Transformer model introduced in the “TabTransformer: Tabular Data Modeling Using Contextual Embeddings” paper.
ExcelFormer	The ExcelFormer model introduced in the “ExcelFormer: A neural network surpassing GBDTs on tabular data” paper.
Trompt	The Trompt model introduced in the “Trompt: Towards a Better Deep Neural Network for Tabular Data” paper.
TransTab	End-to-end TransTab model for downstream prediction tasks from the “TransTab: Learning Transferable Tabular Transformers Across Tables” paper.

BRIDGE	The BRIDGE method from the “rLLM: Relational Table Learning with LLMs” paper.
RDL	Relational Deep Learning (RDL) model from the “RelBench: A Benchmark for Deep Learning on Relational Databases” paper.
RelGNN	The relational table learning model with RelGNN as the HGNN backbone from the “RelGNN: Composite Message Passing for Relational Deep Learning” paper.