Graph neural networks (GNNs) have shown promise in addressing graph-related
problems, including node classification. However, conventional GNNs assume an
even distribution of data across classes, which is often not the case in
real-world scenarios, where certain classes are severely underrepresented. This
leads to suboptimal performance of standard GNNs on imbalanced graphs. In this
paper, we introduce a unique approach that tackles imbalanced classification on
graphs by considering graph heterophily. We investigate the intricate
relationship between class imbalance and graph heterophily, revealing that
minority classes not only exhibit a scarcity of samples but also manifest lower
levels of homophily, facilitating the propagation of erroneous information
among neighboring nodes. Drawing upon this insight, we propose an efficient
method, called Fast Im-GBK, which integrates an imbalance classification
strategy with heterophily-aware GNNs to effectively address the class imbalance
problem while significantly reducing training time. Our experiments on
real-world graphs demonstrate our model's superiority in classification
performance and efficiency for node classification tasks compared to existing
baselines.

在这篇论文中，我们介绍了一种独特的方法，通过考虑图异质性来解决图中的不平衡分类问题。我们研究了类别不平衡和图异质性之间的错综复杂关系，发现少数类别不仅样本稀缺，而且表现出较低的同质性水平，这导致错误信息在相邻节点之间传播。基于这一洞察，我们提出了一种名为 Fast Im-GBK 的高效方法，将不平衡分类策略与意识到异质性的图神经网络相结合，以有效解决类别不平衡问题，并显著减少训练时间。我们在实际图数据上的实验证明了我们的模型在节点分类任务中相比现有基线方法具有更好的分类性能和效率。

基于异质性的图神经网络用于不平衡分类

Heterophily-Based Graph Neural Network for Imbalanced Classification

While graph heterophily has been extensively studied in recent years, a
fundamental research question largely remains nascent: How and to what extent
will graph heterophily affect the prediction performance of graph neural
networks (GNNs)? In this paper, we aim to demystify the impact of graph
heterophily on GNN spectral filters. Our theoretical results show that it is
essential to design adaptive polynomial filters that adapts different degrees
of graph heterophily to guarantee the generalization performance of GNNs.
Inspired by our theoretical findings, we propose a simple yet powerful GNN
named GPatcher by leveraging the MLP-Mixer architectures. Our approach
comprises two main components: (1) an adaptive patch extractor function that
automatically transforms each node's non-Euclidean graph representations to
Euclidean patch representations given different degrees of heterophily, and (2)
an efficient patch mixer function that learns salient node representation from
both the local context information and the global positional information.
Through extensive experiments, the GPatcher model demonstrates outstanding
performance on node classification compared with popular homophily GNNs and
state-of-the-art heterophily GNNs.

本文研究了图异质性对图神经网络性能的影响，提出通过设计自适应多项式滤波器和采用 MLP-Mixer 架构的 GPatcher 模型来解决这个问题，并在节点分类任务上取得了较好的性能。

GPatcher：用于缓解图异质性的简单自适应 MLP 模型

GPatcher: A Simple and Adaptive MLP Model for Alleviating Graph  Heterophily

Graph Neural Networks (GNNs) have shown remarkable performance on
graph-structured data. However, recent empirical studies suggest that GNNs are
very susceptible to distribution shift. There is still significant ambiguity
about why graph-based models seem more vulnerable to these shifts. In this work
we provide a thorough theoretical analysis on it by quantifying the magnitude
of conditional shift between the input features and the output label. Our
findings show that both graph heterophily and model architecture exacerbate
conditional shifts, leading to performance degradation. To address this, we
propose an approach that involves estimating and minimizing the conditional
shift for unsupervised domain adaptation on graphs. In our controlled synthetic
experiments, our algorithm demonstrates robustness towards distribution shift,
resulting in up to 10% absolute ROC AUC improvement versus the second-best
algorithm. Furthermore, comprehensive experiments on both node classification
and graph classification show its robust performance under various distribution
shifts.

本文研究了图神经网络易受分布变化影响的原因，提出了一种基于条件转移的无监督领域自适应方法，并在合成及实际数据实验中表现出鲁棒性。