Summary:

The paper “Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective” is a research paper that proposes a new perspective to look at the performance degradation of deep graph neural networks (GNNs), which is feature overcorrelation. Feature overcorrelation means that the learned node features are highly correlated and redundant due to the stacked aggregators in deep GNNs. The paper demonstrates the existence and causes of feature overcorrelation through empirical and theoretical studies, and proposes a general framework DeCorr to reduce the feature correlation and enable deeper GNNs. The paper also shows that DeCorr is complementary to existing techniques that tackle the oversmoothing issue, which is another key challenge for deep GNNs. The paper is organized as follows:

• In Section 1, the paper introduces the background and motivation of the study, and defines the problem of feature overcorrelation.
• In Section 2, the paper reviews the related work on GNNs, oversmoothing, and feature correlation.
• In Section 3, the paper presents the empirical evidence of feature overcorrelation in deep GNNs, and analyzes its potential reasons from three aspects: aggregation mechanism, activation function, and graph structure.
• In Section 4, the paper proposes the DeCorr framework, which consists of two components: a correlation regularization term and a correlation-aware aggregator. The paper also provides theoretical analysis on the effectiveness of DeCorr.
• In Section 5, the paper conducts extensive experiments on various benchmark datasets and tasks to evaluate the performance of DeCorr and compare it with existing methods. The paper also conducts ablation studies and parameter sensitivity analysis to validate the design choices of DeCorr.
• In Section 6, the paper concludes the paper and discusses some future directions. This is a new work in this area. The paper is the first to propose the concept of feature overcorrelation in deep graph neural networks, and to provide empirical and theoretical evidence for its existence and causes. The paper is also the first to propose a general framework DeCorr to reduce the feature correlation and enable deeper GNNs.

Top Contributions:

The top 3 contributions of the paper are:

• The paper is the first to propose the concept of feature overcorrelation in deep graph neural networks, and to provide empirical and theoretical evidence for its existence and causes.
• The paper is the first to propose a general framework DeCorr to reduce the feature correlation and enable deeper GNNs. DeCorr consists of two components: a correlation regularization term and a correlation-aware aggregator.
• The paper is the first to show that DeCorr is complementary to existing techniques that tackle the oversmoothing issue, which is another key challenge for deep GNNs.

Specific Comments:

• The paper is novel in proposing the concept of feature overcorrelation in deep graph neural networks, which is different from the existing notion of oversmoothing. The paper is also novel in providing empirical and theoretical evidence for the existence and causes of feature overcorrelation in deep GNNs. The paper is also novel in proposing a general framework DeCorr to reduce the feature correlation and enable deeper GNNs. The paper has covered most of the related work on GNNs, oversmoothing, and feature correlation. Some of the key assumptions of the paper are:
• The paper assumes that feature overcorrelation is a more general and fundamental issue than oversmoothing, and that oversmoothing is a special case of feature overcorrelation. The paper also assumes that feature overcorrelation is caused by the stacked aggregators, the activation function, and the graph structure in deep GNNs.
• The paper assumes that reducing the feature correlation can improve the performance of deep GNNs, and that DeCorr can effectively reduce the feature correlation by adding a correlation regularization term and a correlation-aware aggregator.
• The paper assumes that DeCorr is complementary to existing techniques that tackle the oversmoothing issue, and that DeCorr can help enable deeper GNNs. These assumptions are realistic to some extent, but they may only hold for some cases or scenarios. Some possible changes or challenges to these assumptions are:
• Feature overcorrelation may not be the only or the main reason for the performance degradation of deep GNNs. There may be other factors or challenges that affect the learning ability or generalization ability of deep GNNs, graph heterogeneity, etc.
• DeCorr may only be complementary to some existing techniques that tackle the oversmoothing issue. I did not find any apparent technical flaws in the paper. The paper is well-written, well-organized, and well-supported by empirical and theoretical evidence. The paper also provides extensive experiments, ablation studies, parameter sensitivity analysis, and comparisons with existing methods to validate its approach and claims. Some possible gaps are:
• The paper needs a clearer definition, formalization, and measurement of feature overcorrelation.
• The paper does not evaluate or explain the parameter sensitivity analysis of DeCorr. I thought the paper was really cool in proposing a new perspective to look at the performance degradation of deep graph neural networks, which is feature overcorrelation. I also thought the paper was really cool in proposing a general framework DeCorr to reduce the feature correlation and enable deeper GNNs.

Detailed Analysis of Solution Approach:

The key features of the solution in the paper are:

• The paper proposes a new perspective to look at the performance degradation of deep graph neural networks, which is feature overcorrelation. Feature overcorrelation means that the learned node features are highly correlated and redundant due to the stacked aggregators in deep GNNs. The paper demonstrates the existence and causes of feature overcorrelation through empirical and theoretical studies.
• The paper proposes a general framework DeCorr to reduce the feature correlation and enable deeper GNNs. DeCorr consists of two components: a correlation regularization term and a correlation-aware aggregator. The correlation regularization term penalizes the feature correlation between different nodes or layers, while the correlation-aware aggregator adaptively adjusts the aggregation weights based on the feature correlation.
• The paper shows that DeCorr is complementary to existing techniques that tackle the oversmoothing issue, which is another key challenge for deep GNNs. Oversmoothing means that the learned node features are highly similar or indistinguishable due to the stacked aggregators in deep GNNs. The paper shows that DeCorr can help enable deeper GNNs and improve their performance on various benchmark datasets and tasks. Some possible ways to improve the solution are:
• The paper could provide a clear definition or formalization of feature overcorrelation, and how it can be measured or quantified and with other related concepts such as overfitting.
• The paper could provide a thorough evaluation or explanation of the parameter sensitivity analysis of DeCorr. The paper could also provide some insights or guidelines on how to tune these parameters for different datasets or tasks.

Detailed Analysis of Validation and Experiments conducted:

The experiments conducted to validate the solution in the paper are:

• The paper evaluates the performance of DeCorr on various benchmark datasets and tasks, such as node classification, graph classification, and link prediction. The paper compares DeCorr with several existing methods that tackle the oversmoothing issue, such as JK-Net, APPNP, and DropEdge. The paper also compares DeCorr with some baseline methods that do not use DeCorr, such as GCN, GAT, and GraphSAGE. The paper shows that DeCorr can improve the performance of deep GNNs on all the datasets and tasks, and outperform the existing methods in most cases.
• The paper conducts ablation studies to validate the design choices of DeCorr, such as the correlation regularization term and the correlation-aware aggregator. The paper shows that both components are essential and effective for reducing the feature correlation and improving the performance of deep GNNs. The paper also shows that DeCorr can work well with different types of aggregators, such as mean, max, or attention.
• The paper conducts parameter sensitivity analysis to investigate how the correlation regularization coefficient and the number of correlation-aware aggregators affect the performance of DeCorr. The paper shows that DeCorr is robust to different values of these parameters, and that there is a trade-off between reducing the feature correlation and preserving the useful information. The experiments are comprehensive in covering different datasets, tasks, methods, and aspects of DeCorr. However, some possible ways to improve or extend the experiments are:
• The paper could use more diverse or challenging datasets or tasks to evaluate DeCorr, such as heterogeneous graphs, dynamic graphs, or graph generation.
• The paper could use more metrics or criteria to evaluate DeCorr, such as feature diversity, feature interpretability, or computational efficiency.
• The paper could provide more details or explanations on how to choose or tune the parameters of DeCorr for different datasets or tasks.

Comments and Possible Extensions:

• The paper inspires me to solve problems in graph representation learning for deep GNNs.
• The paper opens up new directions and challenges, such as defining, measuring, and reducing feature overcorrelation, extending DeCorr to other types of graphs or tasks, and applying DeCorr to other tasks or applications.
• Some possible examples of new problems or extensions are designing a correlation-aware aggregator for heterogeneous or dynamic graphs, using DeCorr to generate realistic and diverse graphs, and using DeCorr to align or summarize multiple graphs.
• Some possible approaches to solve these problems are using a meta-learning framework, a generative adversarial network framework, or a graph neural network framework that can leverage DeCorr as a generator, a discriminator, a feature extractor, or a feature matcher.

Paper Link