Learning unbiased node representations under class-imbalanced graph data is
challenging due to interactions between adjacent nodes. Existing studies have
in common that they compensate the minor class nodes `as a group' according to
their overall quantity (ignoring node connections in graph), which inevitably
increase the false positive cases for major nodes. We hypothesize that the
increase in these false positive cases is highly affected by the label
distribution around each node and confirm it experimentally. In addition, in
order to handle this issue, we propose Topology-Aware Margin (TAM) to reflect
local topology on the learning objective. Our method compares the connectivity
pattern of each node with the class-averaged counter-part and adaptively
adjusts the margin accordingly based on that. Our method consistently exhibits
superiority over the baselines on various node classification benchmark
datasets with representative GNN architectures.

该研究针对存在标签不平衡的图数据，提出了一种新型节点表示学习方法，通过结合局部拓扑信息设计目标函数，实现了对于节点的快速、精准分类。

TAM：面向类别不平衡节点分类的拓扑感知间隔损失

TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification

In recent years, significant work has been done to include fairness
constraints in the training objective of machine learning algorithms. Many
state-of the-art algorithms tackle this challenge by learning a fair
representation which captures all the relevant information to predict the
output Y while not containing any information about a sensitive attribute S. In
this paper, we propose an adversarial algorithm to learn unbiased
representations via the Hirschfeld-Gebelein-Renyi (HGR) maximal correlation
coefficient. We leverage recent work which has been done to estimate this
coefficient by learning deep neural network transformations and use it as a
minmax game to penalize the intrinsic bias in a multi dimensional latent
representation. Compared to other dependence measures, the HGR coefficient
captures more information about the non-linear dependencies with the sensitive
variable, making the algorithm more efficient in mitigating bias in the
representation. We empirically evaluate and compare our approach and
demonstrate significant improvements over existing works in the field.

本文提出了一种通过 Hirschfeld-Gebelein-Renyi (HGR) 最大相关系数生成不带偏见表示的对抗性算法，从而实现了在机器学习算法的训练目标中包含公平性约束。

通过 Rényi 最小化学习无偏表示

Learning Unbiased Representations via Rényi Minimization

A key element in transfer learning is representation learning; if
representations can be developed that expose the relevant factors underlying
the data, then new tasks and domains can be learned readily based on mappings
of these salient factors. We propose that an important aim for these
representations are to be unbiased. Different forms of representation learning
can be derived from alternative definitions of unwanted bias, e.g., bias to
particular tasks, domains, or irrelevant underlying data dimensions. One very
useful approach to estimating the amount of bias in a representation comes from
maximum mean discrepancy (MMD) [5], a measure of distance between probability
distributions. We are not the first to suggest that MMD can be a useful
criterion in developing representations that apply across multiple domains or
tasks [1]. However, in this paper we describe a number of novel applications of
this criterion that we have devised, all based on the idea of developing
unbiased representations. These formulations include: a standard domain
adaptation framework; a method of learning invariant representations; an
approach based on noise-insensitive autoencoders; and a novel form of
generative model.

本文旨在探索如何通过最大平均差异的方法，从不同层面研究表达学习来培养出一种无偏的模型，这些方法包括标准域自适应框架、学习不变表示的方法、基于噪声不敏感自编码器的方法和一种新的生成模型。