Deep learning has achieved remarkable success in graph-related tasks, yet
this accomplishment heavily relies on large-scale high-quality annotated
datasets. However, acquiring such datasets can be cost-prohibitive, leading to
the practical use of labels obtained from economically efficient sources such
as web searches and user tags. Unfortunately, these labels often come with
noise, compromising the generalization performance of deep networks. To tackle
this challenge and enhance the robustness of deep learning models against label
noise in graph-based tasks, we propose a method called ERASE (Error-Resilient
representation learning on graphs for lAbel noiSe tolerancE). The core idea of
ERASE is to learn representations with error tolerance by maximizing coding
rate reduction. Particularly, we introduce a decoupled label propagation method
for learning representations. Before training, noisy labels are pre-corrected
through structural denoising. During training, ERASE combines prototype
pseudo-labels with propagated denoised labels and updates representations with
error resilience, which significantly improves the generalization performance
in node classification. The proposed method allows us to more effectively
withstand errors caused by mislabeled nodes, thereby strengthening the
robustness of deep networks in handling noisy graph data. Extensive
experimental results show that our method can outperform multiple baselines
with clear margins in broad noise levels and enjoy great scalability. Codes are
released at this https URL

通过最大化编码率减少，ERASE 方法通过在图中学习具有容错性的表示来增强深度学习模型对于标签噪声的鲁棒性，结合原型伪标签与传播去噪标签更新表示，并通过预纠正噪声标签以及处理有噪声图数据来显著提高节点分类的泛化性能。

ERASE：图上的容错错误鲁棒表示学习

ERASE: Error-Resilient Representation Learning on Graphs for Label Noise  Tolerance

It has become a popular paradigm to transfer the knowledge of large-scale
pre-trained models to various downstream tasks via fine-tuning the entire model
parameters. However, with the growth of model scale and the rising number of
downstream tasks, this paradigm inevitably meets the challenges in terms of
computation consumption and memory footprint issues. Recently,
Parameter-Efficient Fine-Tuning (PEFT) (e.g., Adapter, LoRA, BitFit) shows a
promising paradigm to alleviate these concerns by updating only a portion of
parameters. Despite these PEFTs having demonstrated satisfactory performance in
natural language processing, it remains under-explored for the question of
whether these techniques could be transferred to graph-based tasks with Graph
Transformer Networks (GTNs). Therefore, in this paper, we fill this gap by
providing extensive benchmarks with traditional PEFTs on a range of graph-based
downstream tasks. Our empirical study shows that it is sub-optimal to directly
transfer existing PEFTs to graph-based tasks due to the issue of feature
distribution shift. To address this issue, we propose a novel structure-aware
PEFT approach, named G-Adapter, which leverages graph convolution operation to
introduce graph structure (e.g., graph adjacent matrix) as an inductive bias to
guide the updating process. Besides, we propose Bregman proximal point
optimization to further alleviate feature distribution shift by preventing the
model from aggressive update. Extensive experiments demonstrate that G-Adapter
obtains the state-of-the-art performance compared to the counterparts on nine
graph benchmark datasets based on two pre-trained GTNs, and delivers tremendous
memory footprint efficiency compared to the conventional paradigm.

本文旨在探索将 Parameter-Efficient Fine-Tuning (PEFT) 技术应用于基于图的任务，提出了一种名为 G-Adapter 的新型结构感知 PEFT 方法，并使用 Bregman Proximal Point 方法进一步缓解特征分布变化问题，研究结果表明，G-Adapter 相对于现有的 PEFT 方法，在 9 个基于图的基准数据集上获得了最先进的性能，并且相比于传统的方法提供了巨大的内存节省。

G-Adapter：面向图变换器网络的结构感知参数高效迁移学习

G-Adapter: Towards Structure-Aware Parameter-Efficient Transfer Learning  for Graph Transformer Networks

Given a graph dataset, how can we augment it for accurate graph
classification? Graph augmentation is an essential strategy to improve the
performance of graph-based tasks, and has been widely utilized for analyzing
web and social graphs. However, previous works for graph augmentation either a)
involve the target model in the process of augmentation, losing the
generalizability to other tasks, or b) rely on simple heuristics that lead to
unreliable results. In this work, we introduce five desired properties for
effective augmentation. Then, we propose NodeSam (Node Split and Merge) and
SubMix (Subgraph Mix), two model-agnostic approaches for graph augmentation
that satisfy all desired properties with different motivations. NodeSam makes a
balanced change of the graph structure to minimize the risk of semantic change,
while SubMix mixes random subgraphs of multiple graphs to create rich soft
labels combining the evidence for different classes. Our experiments on social
networks and molecular graphs show that NodeSam and SubMix outperform existing
approaches in graph classification.

本研究提出了两种模型无关的图形数据增强方法 NodeSam 和 SubMix，以优化图形分类任务的性能。我们实验结果表明 NodeSam 和 SubMix 在社交网络和分子图中的表现优于现有方法。