TabGSL: Graph Structure Learning for Tabular Data Prediction

• URL: http://arxiv.org/abs/2305.15843v1
• Date: Thu, 25 May 2023 08:33:48 GMT
• Title: TabGSL: Graph Structure Learning for Tabular Data Prediction
• Authors: Jay Chiehen Liao, Cheng-Te Li
• Abstract summary: We present a novel solution, Tabular Graph Structure Learning (TabGSL), to enhance tabular data prediction. Experiments conducted on 30 benchmark datasets demonstrate that TabGSL markedly outperforms both tree-based models and recent deep learning-based models.
• Score: 10.66048003460524
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work presents a novel approach to tabular data prediction leveraging graph structure learning and graph neural networks. Despite the prevalence of tabular data in real-world applications, traditional deep learning methods often overlook the potentially valuable associations between data instances. Such associations can offer beneficial insights for classification tasks, as instances may exhibit similar patterns of correlations among features and target labels. This information can be exploited by graph neural networks, necessitating robust graph structures. However, existing studies primarily focus on improving graph structure from noisy data, largely neglecting the possibility of deriving graph structures from tabular data. We present a novel solution, Tabular Graph Structure Learning (TabGSL), to enhance tabular data prediction by simultaneously learning instance correlation and feature interaction within a unified framework. This is achieved through a proposed graph contrastive learning module, along with transformer-based feature extractor and graph neural network. Comprehensive experiments conducted on 30 benchmark tabular datasets demonstrate that TabGSL markedly outperforms both tree-based models and recent deep learning-based tabular models. Visualizations of the learned instance embeddings further substantiate the effectiveness of TabGSL.

Related papers

• RAGraph: A General Retrieval-Augmented Graph Learning Framework [35.25522856244149]
  We introduce a novel framework called General Retrieval-Augmented Graph Learning (RAGraph) RAGraph brings external graph data into the general graph foundation model to improve model generalization on unseen scenarios. During inference, the RAGraph adeptly retrieves similar toy graphs based on key similarities in downstream tasks.
  arXiv  Detail & Related papers  (2024-10-31T12:05:21Z)
• TabGraphs: A Benchmark and Strong Baselines for Learning on Graphs with Tabular Node Features [17.277932238538302]
  Tabular machine learning may benefit from graph machine learning methods. graph neural networks (GNNs) can indeed often bring gains in predictive performance. Simple feature preprocessing enables them to compete with and even outperform GNNs.
  arXiv  Detail & Related papers  (2024-09-22T15:53:19Z)
• StructChart: Perception, Structuring, Reasoning for Visual Chart Understanding [58.38480335579541]
  Current chart-related tasks focus on either chart perception which refers to extracting information from the visual charts, or performing reasoning given the extracted data. In this paper, we aim to establish a unified and label-efficient learning paradigm for joint perception and reasoning tasks. Experiments are conducted on various chart-related tasks, demonstrating the effectiveness and promising potential for a unified chart perception-reasoning paradigm.
  arXiv  Detail & Related papers  (2023-09-20T12:51:13Z)
• GraphGLOW: Universal and Generalizable Structure Learning for Graph Neural Networks [72.01829954658889]
  This paper introduces the mathematical definition of this novel problem setting. We devise a general framework that coordinates a single graph-shared structure learner and multiple graph-specific GNNs. The well-trained structure learner can directly produce adaptive structures for unseen target graphs without any fine-tuning.
  arXiv  Detail & Related papers  (2023-06-20T03:33:22Z)
• T2G-Former: Organizing Tabular Features into Relation Graphs Promotes Heterogeneous Feature Interaction [15.464703129175298]
  We propose a novel Graph Estimator, which automatically estimates the relations among tabular features and builds graphs by assigning edges between related features. Based on our proposed Graph Estimator, we present a bespoke Transformer network tailored for tabular learning, called T2G-Former. Our T2G-Former achieves superior performance among DNNs and is competitive with non-deep Gradient Boosted Decision Tree models.
  arXiv  Detail & Related papers  (2022-11-30T10:39:24Z)
• Data augmentation on graphs for table type classification [1.1859913430860336]
  We address the classification of tables using a Graph Neural Network, exploiting the table structure for the message passing algorithm in use. We achieve promising preliminary results, proposing a data augmentation method suitable for graph-based table representation.
  arXiv  Detail & Related papers  (2022-08-23T21:54:46Z)
• Graph Structure Learning with Variational Information Bottleneck [70.62851953251253]
  We propose a novel Variational Information Bottleneck guided Graph Structure Learning framework, namely VIB-GSL. VIB-GSL learns an informative and compressive graph structure to distill the actionable information for specific downstream tasks.
  arXiv  Detail & Related papers  (2021-12-16T14:22:13Z)
• Joint Graph Learning and Matching for Semantic Feature Correspondence [69.71998282148762]
  We propose a joint emphgraph learning and matching network, named GLAM, to explore reliable graph structures for boosting graph matching. The proposed method is evaluated on three popular visual matching benchmarks (Pascal VOC, Willow Object and SPair-71k) It outperforms previous state-of-the-art graph matching methods by significant margins on all benchmarks.
  arXiv  Detail & Related papers  (2021-09-01T08:24:02Z)
• Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning [64.98816284854067]
  Graph-based Semi-Supervised Learning (SSL) aims to transfer the labels of a handful of labeled data to the remaining massive unlabeled data via a graph. A novel GCN-based SSL algorithm is presented in this paper to enrich the supervision signals by utilizing both data similarities and graph structure.
  arXiv  Detail & Related papers  (2020-09-15T13:59:28Z)
• Representation Learning of Graphs Using Graph Convolutional Multilayer Networks Based on Motifs [17.823543937167848]
  mGCMN is a novel framework which utilizes node feature information and the higher order local structure of the graph. It will greatly improve the learning efficiency of the graph neural network and promote a brand-new learning mode establishment.
  arXiv  Detail & Related papers  (2020-07-31T04:18:20Z)
• GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training [62.73470368851127]
  Graph representation learning has emerged as a powerful technique for addressing real-world problems. We design Graph Contrastive Coding -- a self-supervised graph neural network pre-training framework. We conduct experiments on three graph learning tasks and ten graph datasets.
  arXiv  Detail & Related papers  (2020-06-17T16:18:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.