Recent studies successfully learned static graph embeddings that are
structurally fair by preventing the effectiveness disparity of high- and
low-degree vertex groups in downstream graph mining tasks. However, achieving
structure fairness in dynamic graph embedding remains an open problem.
Neglecting degree changes in dynamic graphs will significantly impair embedding
effectiveness without notably improving structure fairness. This is because the
embedding performance of high-degree and low-to-high-degree vertices will
significantly drop close to the generally poorer embedding performance of most
slightly changed vertices in the long-tail part of the power-law distribution.
We first identify biased structural evolutions in a dynamic graph based on the
evolving trend of vertex degree and then propose FairDGE, the first
structurally Fair Dynamic Graph Embedding algorithm. FairDGE learns biased
structural evolutions by jointly embedding the connection changes among
vertices and the long-short-term evolutionary trend of vertex degrees.
Furthermore, a novel dual debiasing approach is devised to encode fair
embeddings contrastively, customizing debiasing strategies for different biased
structural evolutions. This innovative debiasing strategy breaks the
effectiveness bottleneck of embeddings without notable fairness loss. Extensive
experiments demonstrate that FairDGE achieves simultaneous improvement in the
effectiveness and fairness of embeddings.

静态图嵌入、动态图嵌入、结构公平性、偏倚的结构演变和 FairDGE 算法是这篇论文的主要关键词和研究领域。

动态图嵌入中的结构公正性：趋势感知的双重去偏方法

Toward Structure Fairness in Dynamic Graph Embedding: A Trend-aware Dual  Debiasing Approach

In social network, a person located at the periphery region (marginal node)
is likely to be treated unfairly when compared with the persons at the center.
While existing fairness works on graphs mainly focus on protecting sensitive
attributes (e.g., age and gender), the fairness incurred by the graph structure
should also be given attention. On the other hand, the information aggregation
mechanism of graph neural networks amplifies such structure unfairness, as
marginal nodes are often far away from other nodes. In this paper, we focus on
novel fairness incurred by the graph structure on graph neural networks, named
\emph{structure fairness}. Specifically, we first analyzed multiple graphs and
observed that marginal nodes in graphs have a worse performance of downstream
tasks than others in graph neural networks. Motivated by the observation, we
propose \textbf{S}tructural \textbf{Fair} \textbf{G}raph \textbf{N}eural
\textbf{N}etwork (SFairGNN), which combines neighborhood expansion based
structure debiasing with hop-aware attentive information aggregation to achieve
structure fairness. Our experiments show \SFairGNN can significantly improve
structure fairness while maintaining overall performance in the downstream
tasks.

在社交网络中，位于边缘区域（边缘节点）的个人与中心的人相比往往会受到不公平待遇。现有的公平性研究主要侧重于保护敏感属性（如年龄和性别），但是图结构所导致的公平性也应该受到关注。本文针对图神经网络中由图结构引发的新型公平性问题，即 “结构公平性”，通过分析多个图并观察到边缘节点在图神经网络中的下游任务表现较差，提出了一种名为 SFairGNN 的结构公平图神经网络，该网络通过基于邻域扩展的结构去偏置和基于跳数的注意力信息聚合相结合，显著提高了结构公平性同时保持下游任务的整体性能。