Learning node-level representations of heterophilic graphs is crucial for
various applications, including fraudster detection and protein function
prediction. In such graphs, nodes share structural similarity identified by the
equivalence of their connectivity which is implicitly encoded in the form of
higher-order hierarchical information in the graphs. The contrastive methods
are popular choices for learning the representation of nodes in a graph.
However, existing contrastive methods struggle to capture higher-order graph
structures. To address this limitation, we propose a novel multiview
contrastive learning approach that integrates diffusion filters on graphs. By
incorporating multiple graph views as augmentations, our method captures the
structural equivalence in heterophilic graphs, enabling the discovery of hidden
relationships and similarities not apparent in traditional node
representations. Our approach outperforms baselines on synthetic and real
structural datasets, surpassing the best baseline by $16.06\%$ on Cornell,
$3.27\%$ on Texas, and $8.04\%$ on Wisconsin. Additionally, it consistently
achieves superior performance on proximal tasks, demonstrating its
effectiveness in uncovering structural information and improving downstream
applications.

学习异质图的节点级表示对于欺诈者检测和蛋白质功能预测等各种应用至关重要。为了解决传统节点表示无法捕捉高阶图结构的问题，我们提出了一种新颖的多视图对比学习方法，通过在图上集成扩散滤波器，捕捉异质图中的结构等价性，发现传统节点表示中隐含的关系和相似性。我们的方法在合成和真实结构数据集上优于基准方法，在 Cornell 上比最佳基准方法提高了 16.06%，在 Texas 上提高了 3.27%，在 Wisconsin 上提高了 8.04%。此外，我们的方法在近邻任务上始终表现出卓越的性能，证明了其在揭示结构信息和改进下游应用方面的有效性。

非局部图的多分辨结构视图对比学习

Contrastive Learning for Non-Local Graphs with Multi-Resolution  Structural Views

Learning effective protein representations is critical in a variety of tasks
in biology such as predicting protein function or structure. Existing
approaches usually pretrain protein language models on a large number of
unlabeled amino acid sequences and then finetune the models with some labeled
data in downstream tasks. Despite the effectiveness of sequence-based
approaches, the power of pretraining on known protein structures, which are
available in smaller numbers only, has not been explored for protein property
prediction, though protein structures are known to be determinants of protein
function. In this paper, we propose to pretrain protein representations
according to their 3D structures. We first present a simple yet effective
encoder to learn the geometric features of a protein. We pretrain the protein
graph encoder by leveraging multiview contrastive learning and different
self-prediction tasks. Experimental results on both function prediction and
fold classification tasks show that our proposed pretraining methods outperform
or are on par with the state-of-the-art sequence-based methods, while using
much less pretraining data. Our implementation is available at
this https URL

本文提出了利用 3D 蛋白结构进行预训练的蛋白质表示方法，并通过多视图对比学习和自我预测任务，实现了对蛋白质的编码。实验结果表明，该方法不仅可以比现有的基于序列的方法更有效地预测蛋白质的功能和褶叠分类，而且使用的预训练数据更少。

几何结构预训练下的蛋白表征学习

Protein Representation Learning by Geometric Structure Pretraining

Humans view the world through many sensory channels, e.g., the
long-wavelength light channel, viewed by the left eye, or the high-frequency
vibrations channel, heard by the right ear. Each view is noisy and incomplete,
but important factors, such as physics, geometry, and semantics, tend to be
shared between all views (e.g., a "dog" can be seen, heard, and felt). We
investigate the classic hypothesis that a powerful representation is one that
models view-invariant factors. We study this hypothesis under the framework of
multiview contrastive learning, where we learn a representation that aims to
maximize mutual information between different views of the same scene but is
otherwise compact. Our approach scales to any number of views, and is
view-agnostic. We analyze key properties of the approach that make it work,
finding that the contrastive loss outperforms a popular alternative based on
cross-view prediction, and that the more views we learn from, the better the
resulting representation captures underlying scene semantics. Our approach
achieves state-of-the-art results on image and video unsupervised learning
benchmarks. Code is released at: this http URL

本文研究了一个强大的表示形式，该表示形式对场景的多视图和不完整信息进行建模，通过多视图对比学习来提取多个视角得到的公共信息，该方法优于基于视图交叉预测的选择，经过检验实现了最先进的图像和视频无监督学习基准结果。