Unsupervised graph representation learning(GRL) aims to distill diverse graph
information into task-agnostic embeddings without label supervision. Due to a
lack of support from labels, recent representation learning methods usually
adopt self-supervised learning, and embeddings are learned by solving a
handcrafted auxiliary task(so-called pretext task). However, partially due to
the irregular non-Euclidean data in graphs, the pretext tasks are generally
designed under homophily assumptions and cornered in the low-frequency signals,
which results in significant loss of other signals, especially high-frequency
signals widespread in graphs with heterophily. Motivated by this limitation, we
propose a multi-view perspective and the usage of diverse pretext tasks to
capture different signals in graphs into embeddings. A novel framework, denoted
as Multi-view Graph Encoder(MVGE), is proposed, and a set of key designs are
identified. More specifically, a set of new pretext tasks are designed to
encode different types of signals, and a straightforward operation is
propxwosed to maintain both the commodity and personalization in both the
attribute and the structural levels. Extensive experiments on synthetic and
real-world network datasets show that the node representations learned with
MVGE achieve significant performance improvements in three different downstream
tasks, especially on graphs with heterophily. Source code is available at
https://github.com/G-AILab/MVGE.

该研究提出了一个多视角方法和多样化预文本任务引入的框架，即 Multi-view Graph Encoder (MVGE)，以捕捉图形中的不同信号，并在合成和现实数据集上进行了广泛实验，并显示出显着的性能改进。

超越同质性的多视图图形表示学习

Multi-View Graph Representation Learning Beyond Homophily

As a subset of unsupervised representation learning, self-supervised
representation learning adopts self-defined signals as supervision and uses the
learned representation for downstream tasks, such as object detection and image
captioning. Many proposed approaches for self-supervised learning follow
naturally a multi-view perspective, where the input (e.g., original images) and
the self-supervised signals (e.g., augmented images) can be seen as two
redundant views of the data. Building from this multi-view perspective, this
paper provides an information-theoretical framework to better understand the
properties that encourage successful self-supervised learning. Specifically, we
demonstrate that self-supervised learned representations can extract
task-relevant information and discard task-irrelevant information. Our
theoretical framework paves the way to a larger space of self-supervised
learning objective design. In particular, we propose a composite objective that
bridges the gap between prior contrastive and predictive learning objectives,
and introduce an additional objective term to discard task-irrelevant
information. To verify our analysis, we conduct controlled experiments to
evaluate the impact of the composite objectives. We also explore our
framework's empirical generalization beyond the multi-view perspective, where
the cross-view redundancy may not be clearly observed.

本文提供了一种信息理论框架，从多个视角探究自监督学习的特性，并介绍了可以丢弃任务无关信息的任务相关自适应目标函数，为自监督学习的目标设计提供了更加广阔的空间。