A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and Generalizability

Pengyun Wang, Junyu Luo, Yanxin Shen, Siyu Heng, Xiao Luo·June 13, 2024

Summary

This paper presents a comprehensive benchmark for graph pooling methods in graph neural networks, evaluating 15 approaches on 21 diverse datasets for tasks like graph classification, regression, and node classification. The study assesses effectiveness, robustness, and generalizability, revealing that dense pooling is generally better for graph classification and regression, while sparse methods like TopKPool and KMISPool excel in node classification. It highlights the importance of graph pooling for capturing multi-scale structures and shows that performance varies across methods, with some being sensitive to noise and distribution shifts. The benchmark provides valuable insights for researchers, contributes to reproducibility, and the source code is publicly available.

Key findings

7

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the problem of graph pooling in graph neural networks, focusing on its effectiveness, robustness, and generalizability . This paper delves into the understanding of pooling in graph neural networks, exploring various pooling methods and their impact on graph representation learning . While the concept of graph pooling is not new, the paper contributes by providing a comprehensive benchmark analysis of different graph pooling techniques, shedding light on their performance and suitability for graph classification tasks .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the effectiveness, robustness, and generalizability of various graph pooling approaches across different tasks in graph neural networks . The evaluation focuses on graph classification, graph regression, and node classification tasks, assessing the performance using metrics such as accuracy for classification tasks and root mean square error (RMSE) for regression tasks . Additionally, the study evaluates the robustness of graph pooling methods by analyzing structural robustness, feature robustness, and generalizability under size-based and density-based distribution shifts . The paper also compares the efficiency and different parameter choices of the graph pooling approaches to provide insights into their performance .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and Generalizability" proposes several novel ideas, methods, and models related to graph pooling approaches in Graph Neural Networks (GNNs) .

  1. Graph Pooling Approaches: The paper introduces and evaluates 15 graph pooling methods across 21 different graph datasets, systematically assessing their performance in terms of effectiveness, robustness, and generalizability . These graph pooling methods play a crucial role in GNNs by enabling the hierarchical reduction of graph representations, essential for capturing multi-scale structures and long-range dependencies .

  2. Sparse Pooling vs. Dense Pooling: The paper categorizes existing graph pooling approaches into two main categories: sparse pooling and dense pooling . Sparse pooling approaches maintain a constant number of nodes after pooling (O(1)), while dense pooling approaches have the number of nodes proportional to the original node numbers (O(|V|) . The paper selects and evaluates 9 sparse pooling approaches and 6 dense pooling approaches, highlighting their differences in computational resources and complexity .

  3. Benchmarking Graph Pooling Methods: The paper addresses the lack of standardized experimental settings and fair benchmarks for evaluating graph pooling methods . By constructing a comprehensive benchmark that includes various graph pooling methods and datasets, the paper aims to provide valuable insights and guidance for deep geometric learning research . This benchmark allows for a systematic assessment of graph pooling methods across different tasks such as graph classification, graph regression, and node classification .

  4. Efficiency and Parameter Analysis: The paper involves detailed efficiency analysis and parameter analysis of the graph pooling methods to validate their strong capability and applicability in various scenarios . By evaluating the performance of these methods under noise attacks and out-of-distribution shifts, the paper provides a comprehensive understanding of the effectiveness and robustness of graph pooling approaches .

In summary, the paper introduces a comprehensive benchmark for evaluating graph pooling methods, categorizes these methods into sparse and dense pooling approaches, and conducts thorough analyses to assess their performance, efficiency, and robustness in various graph-related tasks . The paper "A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness, and Generalizability" introduces novel graph pooling methods and benchmarks their performance across various tasks, providing valuable insights into the field of graph neural networks (GNNs) .

  1. Characteristics of Graph Pooling Methods:

    • The paper categorizes existing graph pooling approaches into two main categories: sparse pooling and dense pooling based on the number of nodes after pooling . Sparse pooling methods maintain a constant number of nodes after pooling (O(1)), while dense pooling methods have the number of nodes proportional to the original node numbers (O(|V|)) .
    • The graph pooling methods play a crucial role in GNNs by enabling the hierarchical reduction of graph representations, essential for capturing multi-scale structures and long-range dependencies .
  2. Advantages Compared to Previous Methods:

    • The paper evaluates the performance of various graph pooling methods across different tasks, including graph classification, graph regression, and node classification, providing a comprehensive understanding of their effectiveness, robustness, and generalizability .
    • By constructing a standardized benchmark that includes 15 graph pooling methods and 21 different graph datasets, the paper addresses the lack of fair benchmarks for evaluating graph pooling methods, allowing for an impartial and consistent comparison .
    • The benchmark systematically assesses the performance of graph pooling methods under noise attacks and out-of-distribution shifts, providing insights into their applicability in real-world scenarios .
    • The paper recommends GraphConv as the backbone model over GCNConv for both graph classification and node classification tasks due to its higher accuracy and lower computational resource consumption .

In summary, the paper's contributions lie in categorizing graph pooling methods, constructing a comprehensive benchmark, evaluating their performance across various tasks, and providing recommendations based on efficiency and effectiveness analyses .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

In the field of graph pooling benchmarking, there are several related research works and notable researchers mentioned in the document . Noteworthy researchers in this field include Cheng Tan, Siyuan Li, Zhangyang Gao, Wenfei Guan, Zedong Wang, Zicheng Liu, Lirong Wu, and Stan Z Li . The key solution highlighted in the paper recommends using GraphConv as the backbone model over GCNConv for both graph classification and node classification tasks due to its higher accuracy and lower computational resource consumption . Additionally, for graph classification tasks, a larger pooling ratio generally improves results, especially for multi-class classification tasks, while the choice of pooling ratio is less critical for node classification tasks .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the performance of graph pooling methods in three dimensions: effectiveness, robustness, and generalizability . The experiments involved assessing the performance of 15 graph pooling methods across different tasks such as graph classification, graph regression, and node classification . Additionally, the experiments investigated the performance of these graph pooling approaches under potential noise attacks and out-of-distribution shifts in real-world scenarios . The study also included detailed efficiency analysis and parameter analysis to validate the capability and applicability of graph pooling approaches in various scenarios .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is a comprehensive benchmark that includes 15 graph pooling methods and 21 different graph datasets . The graph datasets cover various domains such as molecules, bioinformatics, social networks, and synthetic datasets for tasks like graph classification, graph regression, and node classification .

Regarding the availability of the code, the source code of the benchmark used in the study is open source and can be accessed at https://github.com/goose315/Graph_Pooling_Benchmark .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The paper conducts a comprehensive benchmark evaluation focusing on three key aspects: effectiveness, robustness, and generalizability of graph pooling approaches . The evaluation includes performance comparison across various tasks such as graph classification, graph regression, and node classification, utilizing metrics like accuracy and root mean square error (RMSE) . This thorough evaluation helps in validating the effectiveness of different graph pooling methods in diverse scenarios.

Moreover, the paper evaluates the robustness of graph pooling approaches by studying structural robustness and feature robustness through edge additions and deletions, as well as node feature masking . This analysis provides insights into the resilience of the pooling methods under different perturbations, contributing to the verification of the robustness hypothesis.

Furthermore, the evaluation extends to assessing the generalizability of different pooling methods under real-world scenarios by employing size-based and density-based distribution shifts . This aspect of the evaluation helps in understanding how well the graph pooling methods can adapt to varying data distributions, thereby supporting the generalizability hypothesis.

Overall, the experiments and results presented in the paper offer strong empirical evidence to support the scientific hypotheses related to the effectiveness, robustness, and generalizability of graph pooling approaches. The comprehensive evaluation across multiple tasks and scenarios enhances the credibility and reliability of the findings, contributing significantly to the verification of the scientific hypotheses in the field of graph neural networks.


What are the contributions of this paper?

The paper makes significant contributions in the field of graph pooling benchmarking by addressing the following key aspects:

  • Construction of a Comprehensive Benchmark: The paper constructs a benchmark that includes 15 graph pooling methods and 21 different graph datasets, systematically evaluating the performance of graph pooling methods in terms of effectiveness, robustness, and generalizability .
  • Evaluation of Graph Pooling Approaches: It evaluates the performance of various graph pooling approaches across different tasks such as graph classification, graph regression, and node classification. The evaluation includes assessing their performance under noise attacks and out-of-distribution shifts, as well as efficiency and parameter analysis .
  • Insights for Deep Geometric Learning Research: The extensive experiments conducted in the paper validate the strong capability and applicability of graph pooling approaches in various scenarios, providing valuable insights and guidance for deep geometric learning research .

What work can be continued in depth?

Further research in the field of graph pooling benchmarking can be extended in several directions based on the existing comprehensive study:

  • Effectiveness Analysis: Future work can delve deeper into the effectiveness of different graph pooling methods across various graph machine learning tasks such as graph classification, graph regression, and node classification. This can involve exploring new metrics, refining existing evaluation criteria, and comparing the performance of emerging pooling techniques .
  • Robustness Evaluation: There is room for further investigation into the robustness of graph pooling approaches under different types of noise attacks on graph structures and node attributes. Research can focus on developing more resilient pooling methods and assessing their performance in challenging scenarios to enhance the reliability of graph machine learning models .
  • Generalizability Studies: Future research can concentrate on studying the generalizability of graph pooling methods under diverse out-of-distribution shifts, including variations in graph size and density levels. This can involve exploring novel approaches to improve the adaptability of pooling techniques to different data distributions and settings, thereby enhancing the applicability of graph machine learning models in real-world scenarios .

Tables

8

Introduction
Background
Overview of Graph Neural Networks (GNNs)
Importance of graph pooling in GNN architecture
Objective
To establish a benchmark for graph pooling methods
Evaluate 15 approaches on diverse tasks
Analyze effectiveness, robustness, and generalizability
Methodology
Data Collection
Selection of 21 diverse datasets
Graph classification, regression, and node classification tasks
Data Preprocessing
Standardization and normalization of datasets
Handling imbalances and noise in the data
Methodology Overview
Graph Pooling Approaches
Dense Pooling (e.g., Mean, Max, Sum)
Performance in graph classification and regression
Sparse Pooling (e.g., TopKPool, KMISPool)
Performance in node classification
Multi-scale Pooling (e.g., ASAP, SortPool)
Capturing hierarchical structures
Attention-based Pooling (e.g., Graph Attention Pooling)
Importance assignment to nodes
Evaluation Metrics
Accuracy, F1-score, Mean Absolute Error (for regression)
Robustness to noise and distribution shifts
Generalizability across datasets
Results and Analysis
Comparative analysis of pooling methods
Performance trends across tasks
Sensitivity analysis of methods to varying conditions
Insights and Discussion
Key takeaways for researchers
Importance of graph pooling for different tasks
Reproducibility and open-source contributions
Conclusion
Summary of findings
Implications for future research and GNN development
Recommendations for practitioners
Acknowledgments
Acknowledgment of datasets, tools, and contributors
References
List of cited literature and resources
Basic info
papers
machine learning
artificial intelligence
Advanced features
Insights
What insights does the study offer for researchers working on graph neural networks?
What tasks does the benchmark evaluate graph pooling methods for?
What is the primary focus of the paper?
Which pooling methods are found to be effective for graph classification and regression?

A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and Generalizability

Pengyun Wang, Junyu Luo, Yanxin Shen, Siyu Heng, Xiao Luo·June 13, 2024

Summary

This paper presents a comprehensive benchmark for graph pooling methods in graph neural networks, evaluating 15 approaches on 21 diverse datasets for tasks like graph classification, regression, and node classification. The study assesses effectiveness, robustness, and generalizability, revealing that dense pooling is generally better for graph classification and regression, while sparse methods like TopKPool and KMISPool excel in node classification. It highlights the importance of graph pooling for capturing multi-scale structures and shows that performance varies across methods, with some being sensitive to noise and distribution shifts. The benchmark provides valuable insights for researchers, contributes to reproducibility, and the source code is publicly available.
Mind map
Generalizability across datasets
Robustness to noise and distribution shifts
Accuracy, F1-score, Mean Absolute Error (for regression)
Importance assignment to nodes
Attention-based Pooling (e.g., Graph Attention Pooling)
Capturing hierarchical structures
Multi-scale Pooling (e.g., ASAP, SortPool)
Performance in node classification
Sparse Pooling (e.g., TopKPool, KMISPool)
Performance in graph classification and regression
Dense Pooling (e.g., Mean, Max, Sum)
Handling imbalances and noise in the data
Standardization and normalization of datasets
Graph classification, regression, and node classification tasks
Selection of 21 diverse datasets
Analyze effectiveness, robustness, and generalizability
Evaluate 15 approaches on diverse tasks
To establish a benchmark for graph pooling methods
Importance of graph pooling in GNN architecture
Overview of Graph Neural Networks (GNNs)
List of cited literature and resources
Acknowledgment of datasets, tools, and contributors
Recommendations for practitioners
Implications for future research and GNN development
Summary of findings
Reproducibility and open-source contributions
Importance of graph pooling for different tasks
Key takeaways for researchers
Sensitivity analysis of methods to varying conditions
Performance trends across tasks
Comparative analysis of pooling methods
Evaluation Metrics
Graph Pooling Approaches
Data Preprocessing
Data Collection
Objective
Background
References
Acknowledgments
Conclusion
Insights and Discussion
Results and Analysis
Methodology Overview
Methodology
Introduction
Outline
Introduction
Background
Overview of Graph Neural Networks (GNNs)
Importance of graph pooling in GNN architecture
Objective
To establish a benchmark for graph pooling methods
Evaluate 15 approaches on diverse tasks
Analyze effectiveness, robustness, and generalizability
Methodology
Data Collection
Selection of 21 diverse datasets
Graph classification, regression, and node classification tasks
Data Preprocessing
Standardization and normalization of datasets
Handling imbalances and noise in the data
Methodology Overview
Graph Pooling Approaches
Dense Pooling (e.g., Mean, Max, Sum)
Performance in graph classification and regression
Sparse Pooling (e.g., TopKPool, KMISPool)
Performance in node classification
Multi-scale Pooling (e.g., ASAP, SortPool)
Capturing hierarchical structures
Attention-based Pooling (e.g., Graph Attention Pooling)
Importance assignment to nodes
Evaluation Metrics
Accuracy, F1-score, Mean Absolute Error (for regression)
Robustness to noise and distribution shifts
Generalizability across datasets
Results and Analysis
Comparative analysis of pooling methods
Performance trends across tasks
Sensitivity analysis of methods to varying conditions
Insights and Discussion
Key takeaways for researchers
Importance of graph pooling for different tasks
Reproducibility and open-source contributions
Conclusion
Summary of findings
Implications for future research and GNN development
Recommendations for practitioners
Acknowledgments
Acknowledgment of datasets, tools, and contributors
References
List of cited literature and resources
Key findings
7

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the problem of graph pooling in graph neural networks, focusing on its effectiveness, robustness, and generalizability . This paper delves into the understanding of pooling in graph neural networks, exploring various pooling methods and their impact on graph representation learning . While the concept of graph pooling is not new, the paper contributes by providing a comprehensive benchmark analysis of different graph pooling techniques, shedding light on their performance and suitability for graph classification tasks .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the effectiveness, robustness, and generalizability of various graph pooling approaches across different tasks in graph neural networks . The evaluation focuses on graph classification, graph regression, and node classification tasks, assessing the performance using metrics such as accuracy for classification tasks and root mean square error (RMSE) for regression tasks . Additionally, the study evaluates the robustness of graph pooling methods by analyzing structural robustness, feature robustness, and generalizability under size-based and density-based distribution shifts . The paper also compares the efficiency and different parameter choices of the graph pooling approaches to provide insights into their performance .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and Generalizability" proposes several novel ideas, methods, and models related to graph pooling approaches in Graph Neural Networks (GNNs) .

  1. Graph Pooling Approaches: The paper introduces and evaluates 15 graph pooling methods across 21 different graph datasets, systematically assessing their performance in terms of effectiveness, robustness, and generalizability . These graph pooling methods play a crucial role in GNNs by enabling the hierarchical reduction of graph representations, essential for capturing multi-scale structures and long-range dependencies .

  2. Sparse Pooling vs. Dense Pooling: The paper categorizes existing graph pooling approaches into two main categories: sparse pooling and dense pooling . Sparse pooling approaches maintain a constant number of nodes after pooling (O(1)), while dense pooling approaches have the number of nodes proportional to the original node numbers (O(|V|) . The paper selects and evaluates 9 sparse pooling approaches and 6 dense pooling approaches, highlighting their differences in computational resources and complexity .

  3. Benchmarking Graph Pooling Methods: The paper addresses the lack of standardized experimental settings and fair benchmarks for evaluating graph pooling methods . By constructing a comprehensive benchmark that includes various graph pooling methods and datasets, the paper aims to provide valuable insights and guidance for deep geometric learning research . This benchmark allows for a systematic assessment of graph pooling methods across different tasks such as graph classification, graph regression, and node classification .

  4. Efficiency and Parameter Analysis: The paper involves detailed efficiency analysis and parameter analysis of the graph pooling methods to validate their strong capability and applicability in various scenarios . By evaluating the performance of these methods under noise attacks and out-of-distribution shifts, the paper provides a comprehensive understanding of the effectiveness and robustness of graph pooling approaches .

In summary, the paper introduces a comprehensive benchmark for evaluating graph pooling methods, categorizes these methods into sparse and dense pooling approaches, and conducts thorough analyses to assess their performance, efficiency, and robustness in various graph-related tasks . The paper "A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness, and Generalizability" introduces novel graph pooling methods and benchmarks their performance across various tasks, providing valuable insights into the field of graph neural networks (GNNs) .

  1. Characteristics of Graph Pooling Methods:

    • The paper categorizes existing graph pooling approaches into two main categories: sparse pooling and dense pooling based on the number of nodes after pooling . Sparse pooling methods maintain a constant number of nodes after pooling (O(1)), while dense pooling methods have the number of nodes proportional to the original node numbers (O(|V|)) .
    • The graph pooling methods play a crucial role in GNNs by enabling the hierarchical reduction of graph representations, essential for capturing multi-scale structures and long-range dependencies .
  2. Advantages Compared to Previous Methods:

    • The paper evaluates the performance of various graph pooling methods across different tasks, including graph classification, graph regression, and node classification, providing a comprehensive understanding of their effectiveness, robustness, and generalizability .
    • By constructing a standardized benchmark that includes 15 graph pooling methods and 21 different graph datasets, the paper addresses the lack of fair benchmarks for evaluating graph pooling methods, allowing for an impartial and consistent comparison .
    • The benchmark systematically assesses the performance of graph pooling methods under noise attacks and out-of-distribution shifts, providing insights into their applicability in real-world scenarios .
    • The paper recommends GraphConv as the backbone model over GCNConv for both graph classification and node classification tasks due to its higher accuracy and lower computational resource consumption .

In summary, the paper's contributions lie in categorizing graph pooling methods, constructing a comprehensive benchmark, evaluating their performance across various tasks, and providing recommendations based on efficiency and effectiveness analyses .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

In the field of graph pooling benchmarking, there are several related research works and notable researchers mentioned in the document . Noteworthy researchers in this field include Cheng Tan, Siyuan Li, Zhangyang Gao, Wenfei Guan, Zedong Wang, Zicheng Liu, Lirong Wu, and Stan Z Li . The key solution highlighted in the paper recommends using GraphConv as the backbone model over GCNConv for both graph classification and node classification tasks due to its higher accuracy and lower computational resource consumption . Additionally, for graph classification tasks, a larger pooling ratio generally improves results, especially for multi-class classification tasks, while the choice of pooling ratio is less critical for node classification tasks .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the performance of graph pooling methods in three dimensions: effectiveness, robustness, and generalizability . The experiments involved assessing the performance of 15 graph pooling methods across different tasks such as graph classification, graph regression, and node classification . Additionally, the experiments investigated the performance of these graph pooling approaches under potential noise attacks and out-of-distribution shifts in real-world scenarios . The study also included detailed efficiency analysis and parameter analysis to validate the capability and applicability of graph pooling approaches in various scenarios .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is a comprehensive benchmark that includes 15 graph pooling methods and 21 different graph datasets . The graph datasets cover various domains such as molecules, bioinformatics, social networks, and synthetic datasets for tasks like graph classification, graph regression, and node classification .

Regarding the availability of the code, the source code of the benchmark used in the study is open source and can be accessed at https://github.com/goose315/Graph_Pooling_Benchmark .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The paper conducts a comprehensive benchmark evaluation focusing on three key aspects: effectiveness, robustness, and generalizability of graph pooling approaches . The evaluation includes performance comparison across various tasks such as graph classification, graph regression, and node classification, utilizing metrics like accuracy and root mean square error (RMSE) . This thorough evaluation helps in validating the effectiveness of different graph pooling methods in diverse scenarios.

Moreover, the paper evaluates the robustness of graph pooling approaches by studying structural robustness and feature robustness through edge additions and deletions, as well as node feature masking . This analysis provides insights into the resilience of the pooling methods under different perturbations, contributing to the verification of the robustness hypothesis.

Furthermore, the evaluation extends to assessing the generalizability of different pooling methods under real-world scenarios by employing size-based and density-based distribution shifts . This aspect of the evaluation helps in understanding how well the graph pooling methods can adapt to varying data distributions, thereby supporting the generalizability hypothesis.

Overall, the experiments and results presented in the paper offer strong empirical evidence to support the scientific hypotheses related to the effectiveness, robustness, and generalizability of graph pooling approaches. The comprehensive evaluation across multiple tasks and scenarios enhances the credibility and reliability of the findings, contributing significantly to the verification of the scientific hypotheses in the field of graph neural networks.


What are the contributions of this paper?

The paper makes significant contributions in the field of graph pooling benchmarking by addressing the following key aspects:

  • Construction of a Comprehensive Benchmark: The paper constructs a benchmark that includes 15 graph pooling methods and 21 different graph datasets, systematically evaluating the performance of graph pooling methods in terms of effectiveness, robustness, and generalizability .
  • Evaluation of Graph Pooling Approaches: It evaluates the performance of various graph pooling approaches across different tasks such as graph classification, graph regression, and node classification. The evaluation includes assessing their performance under noise attacks and out-of-distribution shifts, as well as efficiency and parameter analysis .
  • Insights for Deep Geometric Learning Research: The extensive experiments conducted in the paper validate the strong capability and applicability of graph pooling approaches in various scenarios, providing valuable insights and guidance for deep geometric learning research .

What work can be continued in depth?

Further research in the field of graph pooling benchmarking can be extended in several directions based on the existing comprehensive study:

  • Effectiveness Analysis: Future work can delve deeper into the effectiveness of different graph pooling methods across various graph machine learning tasks such as graph classification, graph regression, and node classification. This can involve exploring new metrics, refining existing evaluation criteria, and comparing the performance of emerging pooling techniques .
  • Robustness Evaluation: There is room for further investigation into the robustness of graph pooling approaches under different types of noise attacks on graph structures and node attributes. Research can focus on developing more resilient pooling methods and assessing their performance in challenging scenarios to enhance the reliability of graph machine learning models .
  • Generalizability Studies: Future research can concentrate on studying the generalizability of graph pooling methods under diverse out-of-distribution shifts, including variations in graph size and density levels. This can involve exploring novel approaches to improve the adaptability of pooling techniques to different data distributions and settings, thereby enhancing the applicability of graph machine learning models in real-world scenarios .
Tables
8
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