Towards Causal Classification: A Comprehensive Study on Graph Neural Networks

• URL: http://arxiv.org/abs/2401.15444v1
• Date: Sat, 27 Jan 2024 15:35:05 GMT
• Title: Towards Causal Classification: A Comprehensive Study on Graph Neural Networks
• Authors: Simi Job, Xiaohui Tao, Taotao Cai, Lin Li, Haoran Xie, Jianming Yong
• Abstract summary: Graph Neural Networks (GNNs) for processing graph-structured data have expanded their potential for causal analysis. Our study delves into nine benchmark graph classification models, testing their strength and versatility across seven datasets. Our findings are instrumental in furthering the understanding and practical application of GNNs in diverse datacentric fields.
• Score: 9.360596957822471
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The exploration of Graph Neural Networks (GNNs) for processing graph-structured data has expanded, particularly their potential for causal analysis due to their universal approximation capabilities. Anticipated to significantly enhance common graph-based tasks such as classification and prediction, the development of a causally enhanced GNN framework is yet to be thoroughly investigated. Addressing this shortfall, our study delves into nine benchmark graph classification models, testing their strength and versatility across seven datasets spanning three varied domains to discern the impact of causality on the predictive prowess of GNNs. This research offers a detailed assessment of these models, shedding light on their efficiency, and flexibility in different data environments, and highlighting areas needing advancement. Our findings are instrumental in furthering the understanding and practical application of GNNs in diverse datacentric fields

Related papers

• Uncertainty in Graph Neural Networks: A Survey [50.63474656037679]
  Graph Neural Networks (GNNs) have been extensively used in various real-world applications. However, the predictive uncertainty of GNNs stemming from diverse sources can lead to unstable and erroneous predictions. This survey aims to provide a comprehensive overview of the GNNs from the perspective of uncertainty.
  arXiv  Detail & Related papers  (2024-03-11T21:54:52Z)
• DGNN: Decoupled Graph Neural Networks with Structural Consistency between Attribute and Graph Embedding Representations [62.04558318166396]
  Graph neural networks (GNNs) demonstrate a robust capability for representation learning on graphs with complex structures. A novel GNNs framework, dubbed Decoupled Graph Neural Networks (DGNN), is introduced to obtain a more comprehensive embedding representation of nodes. Experimental results conducted on several graph benchmark datasets verify DGNN's superiority in node classification task.
  arXiv  Detail & Related papers  (2024-01-28T06:43:13Z)
• Rethinking Causal Relationships Learning in Graph Neural Networks [24.7962807148905]
  We introduce a lightweight and adaptable GNN module designed to strengthen GNNs' causal learning capabilities. We empirically validate the effectiveness of the proposed module.
  arXiv  Detail & Related papers  (2023-12-15T08:54:32Z)
• Information Flow in Graph Neural Networks: A Clinical Triage Use Case [49.86931948849343]
  Graph Neural Networks (GNNs) have gained popularity in healthcare and other domains due to their ability to process multi-modal and multi-relational graphs. We investigate how the flow of embedding information within GNNs affects the prediction of links in Knowledge Graphs (KGs) Our results demonstrate that incorporating domain knowledge into the GNN connectivity leads to better performance than using the same connectivity as the KG or allowing unconstrained embedding propagation.
  arXiv  Detail & Related papers  (2023-09-12T09:18:12Z)
• The Expressive Power of Graph Neural Networks: A Survey [9.08607528905173]
  We conduct a first survey for models for enhancing expressive power under different forms of definition. The models are reviewed based on three categories, i.e., Graph feature enhancement, Graph topology enhancement, and GNNs architecture enhancement.
  arXiv  Detail & Related papers  (2023-08-16T09:12:21Z)
• A Survey on Explainability of Graph Neural Networks [4.612101932762187]
  Graph neural networks (GNNs) are powerful graph-based deep-learning models. This survey aims to provide a comprehensive overview of the existing explainability techniques for GNNs.
  arXiv  Detail & Related papers  (2023-06-02T23:36:49Z)
• Size Generalization of Graph Neural Networks on Biological Data: Insights and Practices from the Spectral Perspective [16.01608638659267]
  We investigate size-induced distribution shifts in graphs and assess their impact on the ability of graph neural networks (GNNs) to generalize to larger graphs. We introduce a simple yet effective model-agnostic strategy, which makes GNNs aware of important subgraph patterns to enhance their size generalizability.
  arXiv  Detail & Related papers  (2023-05-24T23:01:14Z)
• DEGREE: Decomposition Based Explanation For Graph Neural Networks [55.38873296761104]
  We propose DEGREE to provide a faithful explanation for GNN predictions. By decomposing the information generation and aggregation mechanism of GNNs, DEGREE allows tracking the contributions of specific components of the input graph to the final prediction. We also design a subgraph level interpretation algorithm to reveal complex interactions between graph nodes that are overlooked by previous methods.
  arXiv  Detail & Related papers  (2023-05-22T10:29:52Z)
• Graph Neural Networks for Graphs with Heterophily: A Survey [98.45621222357397]
  We provide a comprehensive review of graph neural networks (GNNs) for heterophilic graphs. Specifically, we propose a systematic taxonomy that essentially governs existing heterophilic GNN models. We discuss the correlation between graph heterophily and various graph research domains, aiming to facilitate the development of more effective GNNs.
  arXiv  Detail & Related papers  (2022-02-14T23:07:47Z)
• Towards a Taxonomy of Graph Learning Datasets [10.151886932716518]
  Graph neural networks (GNNs) have attracted much attention due to their ability to leverage the intrinsic geometries of the underlying data. Here, we provide a principled approach to taxonomize graph benchmarking datasets by carefully designing a collection of graph perturbations. Our data-driven taxonomization of graph datasets provides a new understanding of critical dataset characteristics.
  arXiv  Detail & Related papers  (2021-10-27T23:08:01Z)
• Towards Deeper Graph Neural Networks [63.46470695525957]
  Graph convolutions perform neighborhood aggregation and represent one of the most important graph operations. Several recent studies attribute this performance deterioration to the over-smoothing issue. We propose Deep Adaptive Graph Neural Network (DAGNN) to adaptively incorporate information from large receptive fields.
  arXiv  Detail & Related papers  (2020-07-18T01:11:14Z)

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.