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XGBoost-Enhanced Graph Neural Networks: A New Architecture for Heterogeneous Tabular Data

Applied Sciences (Switzerland), ISSN: 2076-3417, Vol: 14, Issue: 13
2024
  • 2
    Citations
  • 0
    Usage
  • 11
    Captures
  • 1
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    2
  • Captures
    11
  • Mentions
    1
    • News Mentions
      1
      • 1

Most Recent News

Harbin University of Commerce Researchers Publish New Data on Applied Sciences (XGBoost-Enhanced Graph Neural Networks: A New Architecture for Heterogeneous Tabular Data)

2024 JUL 29 (NewsRx) -- By a News Reporter-Staff News Editor at NewsRx Science Daily -- Researchers detail new data in applied sciences. According to

Article Description

Graph neural networks (GNNs) perform well in text analysis tasks. Their unique structure allows them to capture complex patterns and dependencies in text, making them ideal for processing natural language tasks. At the same time, XGBoost (version 1.6.2.) outperforms other machine learning methods on heterogeneous tabular data. However, traditional graph neural networks mainly study isomorphic and sparse data features. Therefore, when dealing with tabular data, traditional graph neural networks encounter challenges such as data structure mismatch, feature selection, and processing difficulties. To solve these problems, we propose a novel architecture, XGNN, which combines the advantages of XGBoost and GNNs to deal with heterogeneous features and graph structures. In this paper, we use GAT for our graph neural network model. We can train XGBoost and GNN end-to-end to fit and adjust the new tree in XGBoost based on the gradient information from the GNN. Extensive experiments on node prediction and node classification tasks demonstrate that the performance of our proposed new model is significantly improved for both prediction and classification tasks and performs particularly well on heterogeneous tabular data.

Bibliographic Details

Liuxi Yan; Yaoqun Xu

MDPI AG

Materials Science; Physics and Astronomy; Engineering; Chemical Engineering; Computer Science

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