This paper examines how a notion of stable explanation developed elsewhere in
Defeasible Logic can be expressed in the context of formal argumentation. With
this done, we discuss the deontic meaning of this reconstruction and show how
to build from argumentation neighborhood structures for deontic logic where
this notion of explanation can be characterised. Some direct complexity results
are offered.

本文研究了如何在正式论证的情境下表达并建立稳定解释的概念，并展示了如何利用论证邻域结构构建符合行为准则逻辑的解释，同时提供了一些复杂性结果。

稳定的规范解释：从论证到义务逻辑

Stable Normative Explanations: From Argumentation to Deontic Logic

Conventional graph neural networks (GNNs) are often confronted with fairness
issues that may stem from their input, including node attributes and neighbors
surrounding a node. While several recent approaches have been proposed to
eliminate the bias rooted in sensitive attributes, they ignore the other key
input of GNNs, namely the neighbors of a node, which can introduce bias since
GNNs hinge on neighborhood structures to generate node representations. In
particular, the varying neighborhood structures across nodes, manifesting
themselves in drastically different node degrees, give rise to the diverse
behaviors of nodes and biased outcomes. In this paper, we first define and
generalize the degree bias using a generalized definition of node degree as a
manifestation and quantification of different multi-hop structures around
different nodes. To address the bias in the context of node classification, we
propose a novel GNN framework called Generalized Degree Fairness-centric Graph
Neural Network (Deg-FairGNN). Specifically, in each GNN layer, we employ a
learnable debiasing function to generate debiasing contexts, which modulate the
layer-wise neighborhood aggregation to eliminate the degree bias originating
from the diverse degrees among nodes. Extensive experiments on three benchmark
datasets demonstrate the effectiveness of our model on both accuracy and
fairness metrics.

本文针对传统图神经网络中存在的公平性问题，提出了一种新的 GNN 框架，使用可学习的去偏函数来消除不同节点间的度数差异所导致的偏差，以解决节点分类问题中存在的偏差。

图神经网络中广义程度公平性的研究

On Generalized Degree Fairness in Graph Neural Networks

Graph neural networks (GNNs) have emerged as a powerful paradigm for
embedding-based entity alignment due to their capability of identifying
isomorphic subgraphs. However, in real knowledge graphs (KGs), the counterpart
entities usually have non-isomorphic neighborhood structures, which easily
causes GNNs to yield different representations for them. To tackle this
problem, we propose a new KG alignment network, namely AliNet, aiming at
mitigating the non-isomorphism of neighborhood structures in an end-to-end
manner. As the direct neighbors of counterpart entities are usually dissimilar
due to the schema heterogeneity, AliNet introduces distant neighbors to expand
the overlap between their neighborhood structures. It employs an attention
mechanism to highlight helpful distant neighbors and reduce noises. Then, it
controls the aggregation of both direct and distant neighborhood information
using a gating mechanism. We further propose a relation loss to refine entity
representations. We perform thorough experiments with detailed ablation studies
and analyses on five entity alignment datasets, demonstrating the effectiveness
of AliNet.

本研究提出了一种名为 AliNet 的知识图谱对齐网络，通过引入远程邻居扩展对应实体的邻域，并采用注意力机制和门控机制，控制了直接邻居和远端邻居信息的整合，进而通过关系损失优化实体表示。在五个实体对齐数据集上的实验证明了 AliNet 的有效性。