Graph neural networks (GNNs) excel in learning from network-like data but
often lack interpretability, making their application challenging in domains
requiring transparent decision-making. We propose the Graph Kolmogorov-Arnold
Network (GKAN), a novel GNN model leveraging spline-based activation functions
on edges to enhance both accuracy and interpretability. Our experiments on five
benchmark datasets demonstrate that GKAN outperforms state-of-the-art GNN
models in node classification, link prediction, and graph classification tasks.
In addition to the improved accuracy, GKAN's design inherently provides clear
insights into the model's decision-making process, eliminating the need for
post-hoc explainability techniques. This paper discusses the methodology,
performance, and interpretability of GKAN, highlighting its potential for
applications in domains where interpretability is crucial.

Graph neural network 模型 GKAN 通过使用基于样条函数的边缘激活函数，提高了准确性和解释性，在节点分类、链接预测和图分类任务上胜过了最先进的图神经网络模型。此外，GKAN 的设计本身提供了对模型决策过程的清晰见解，消除了事后解释性技术的需求。

科尔莫戈洛夫 - 阿诺德图神经网络

Kolmogorov-Arnold Graph Neural Networks

Automated decision systems (ADS) are increasingly used for consequential
decision-making. These systems often rely on sophisticated yet opaque machine
learning models, which do not allow for understanding how a given decision was
arrived at. This is not only problematic from a legal perspective, but
non-transparent systems are also prone to yield unfair outcomes because their
sanity is challenging to assess and calibrate in the first place -- which is
particularly worrisome for human decision-subjects. Based on this observation
and building upon existing work, I aim to make the following three main
contributions through my doctoral thesis: (a) understand how (potential)
decision-subjects perceive algorithmic decisions (with varying degrees of
transparency of the underlying ADS), as compared to similar decisions made by
humans; (b) evaluate different tools for transparent decision-making with
respect to their effectiveness in enabling people to appropriately assess the
quality and fairness of ADS; and (c) develop human-understandable technical
artifacts for fair automated decision-making. Over the course of the first half
of my PhD program, I have already addressed substantial pieces of (a) and (c),
whereas (b) will be the major focus of the second half.

该论文探讨了自动化决策系统在人类决策中的作用和不足，旨在通过深入研究来理解决策主体对于基于自动化决策系统的透明度的决策的不同感受，确保决策的质量、公正以及人性化。