Network systems form the foundation of modern society, playing a critical
role in various applications. However, these systems are at significant risk of
being adversely affected by unforeseen circumstances, such as disasters.
Considering this, there is a pressing need for research to enhance the
robustness of network systems. Recently, in reinforcement learning, the
relationship between acquiring robustness and regularizing entropy has been
identified. Additionally, imitation learning is used within this framework to
reflect experts' behavior. However, there are no comprehensive studies on the
use of a similar imitation framework for optimal transport on networks.
Therefore, in this study, imitation-regularized optimal transport (I-OT) on
networks was investigated. It encodes prior knowledge on the network by
imitating a given prior distribution. The I-OT solution demonstrated robustness
in terms of the cost defined on the network. Moreover, we applied the I-OT to a
logistics planning problem using real data. We also examined the imitation and
apriori risk information scenarios to demonstrate the usefulness and
implications of the proposed method.

通过在网络上模拟学习、应用强化学习和逆向学习的方法，研究人员探索了网络系统在提高稳健性方面的新途径，并利用先验知识模拟了给定先验分布，证明了这种模型的稳健性和应用价值。

网络上的模仿正则化最优输运：可证明的鲁棒性与物流规划应用

Imitation-regularized Optimal Transport on Networks: Provable Robustness  and Application to Logistics Planning

The black-box nature of artificial intelligence (AI) models has been the
source of many concerns in their use for critical applications. Explainable
Artificial Intelligence (XAI) is a rapidly growing research field that aims to
create machine learning models that can provide clear and interpretable
explanations for their decisions and actions. In the field of network
cybersecurity, XAI has the potential to revolutionize the way we approach
network security by enabling us to better understand the behavior of cyber
threats and to design more effective defenses. In this survey, we review the
state of the art in XAI for cybersecurity in network systems and explore the
various approaches that have been proposed to address this important problem.
The review follows a systematic classification of network-driven cybersecurity
threats and issues. We discuss the challenges and limitations of current XAI
methods in the context of cybersecurity and outline promising directions for
future research.

通过对网络系统中的网络驱动安全威胁和问题进行系统分类，本文就网络安全中的可解释人工智能问题进行了回顾和研究，探讨了解释人工智能目前的挑战和局限性，并提出了未来的研究方向。

网络安全可解释人工智能调查

A Survey on Explainable Artificial Intelligence for Network Cybersecurity

The effort to understand network systems in increasing detail has resulted in
a diversity of methods designed to extract their large-scale structure from
data. Unfortunately, many of these methods yield diverging descriptions of the
same network, making both the comparison and understanding of their results a
difficult challenge. A possible solution to this outstanding issue is to shift
the focus away from ad hoc methods and move towards more principled approaches
based on statistical inference of generative models. As a result, we face
instead the more well-defined task of selecting between competing generative
processes, which can be done under a unified probabilistic framework. Here, we
consider the comparison between a variety of generative models including
features such as degree correction, where nodes with arbitrary degrees can
belong to the same group, and community overlap, where nodes are allowed to
belong to more than one group. Because such model variants possess an
increasing number of parameters, they become prone to overfitting. In this
work, we present a method of model selection based on the minimum description
length criterion and posterior odds ratios that is capable of fully accounting
for the increased degrees of freedom of the larger models, and selects the best
one according to the statistical evidence available in the data. In applying
this method to many empirical unweighted networks from different fields, we
observe that community overlap is very often not supported by statistical
evidence and is selected as a better model only for a minority of them. On the
other hand, we find that degree correction tends to be almost universally
favored by the available data, implying that intrinsic node proprieties (as
opposed to group properties) are often an essential ingredient of network
formation.

该研究使用最小描述长度标准和后验概率比方法，基于统计学推断从许多生成模型中选择最佳的模型，结果表明，节点内部的属性通常比群体属性更重要，度修正模型通常是大多数无权网络的最佳模型。