TabGNN: Multiplex Graph Neural Network for Tabular Data Prediction

08/20/2021
by   Xiawei Guo, et al.
0

Tabular data prediction (TDP) is one of the most popular industrial applications, and various methods have been designed to improve the prediction performance. However, existing works mainly focus on feature interactions and ignore sample relations, e.g., users with the same education level might have a similar ability to repay the debt. In this work, by explicitly and systematically modeling sample relations, we propose a novel framework TabGNN based on recently popular graph neural networks (GNN). Specifically, we firstly construct a multiplex graph to model the multifaceted sample relations, and then design a multiplex graph neural network to learn enhanced representation for each sample. To integrate TabGNN with the tabular solution in our company, we concatenate the learned embeddings and the original ones, which are then fed to prediction models inside the solution. Experiments on eleven TDP datasets from various domains, including classification and regression ones, show that TabGNN can consistently improve the performance compared to the tabular solution AutoFE in 4Paradigm.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/07/2022

Explicit Feature Interaction-aware Graph Neural Networks

Graph neural networks are powerful methods to handle graph-structured da...
research
07/13/2022

Graph Property Prediction on Open Graph Benchmark: A Winning Solution by Graph Neural Architecture Search

Aiming at two molecular graph datasets and one protein association subgr...
research
05/17/2021

Improving Graph Neural Networks with Simple Architecture Design

Graph Neural Networks have emerged as a useful tool to learn on the data...
research
07/27/2022

Gaia: Graph Neural Network with Temporal Shift aware Attention for Gross Merchandise Value Forecast in E-commerce

E-commerce has gone a long way in empowering merchants through the inter...
research
03/07/2022

Graph Neural Networks for Image Classification and Reinforcement Learning using Graph representations

In this paper, we will evaluate the performance of graph neural networks...
research
05/31/2022

Strategic Classification with Graph Neural Networks

Strategic classification studies learning in settings where users can mo...
research
12/03/2019

Neural Network Branching for Neural Network Verification

Formal verification of neural networks is essential for their deployment...

Please sign up or login with your details

Forgot password? Click here to reset