In many modern data science problems, data are represented by a graph (network), e.g., social, biological, and communication networks. Over the past decade, numerous signal processing and machine learning (ML) algorithms have been introduced for analyzing graph structured data. With the growth of interest in graphs and graph-based learning tasks in a variety of applications, there is a need to explore explainability in graph data science. In this article, we aim to approach the issue of explainable graph data science, focusing on one of the most fundamental learning tasks, community detection, as it is usually the first step in extracting information from graphs. A community is a dense subnetwork within a larger network that corresponds to a specific function. Despite the success of different community detection methods on synthetic networks with strong modular structure, much remains unknown about the quality and significance of the outputs of these algorithms when applied to real-world networks with unknown modular structure. Inspired by recent advances in explainable artificial intelligence (AI) and ML, in this article, we present methods and metrics from network science to quantify three different aspects of explainability, i.e., interpretability, replicability, and reproducibility, in the context of community detection.

中文翻译：

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图数据科学中的可解释性：社区检测的可解释性、可复制性和可重复性


在许多现代数据科学问题中，数据由图（网络）表示，例如社交网络、生物网络和通信网络。在过去的十年中，已经引入了大量的信号处理和机器学习（ML）算法来分析图结构化数据。随着各种应用中对图和基于图的学​​习任务的兴趣的增长，有必要探索图数据科学的可解释性。在本文中，我们的目标是解决可解释的图数据科学问题，重点关注最基本的学习任务之一——社区检测，因为它通常是从图中提取信息的第一步。社区是较大网络中对应于特定功能的密集子网络。尽管不同的社区检测方法在具有强大模块化结构的合成网络上取得了成功，但当这些算法应用于具有未知模块化结构的现实网络时，这些算法输出的质量和意义仍然未知。受可解释人工智能 (AI) 和 ML 最新进展的启发，在本文中，我们提出了网络科学的方法和指标，以在社区检测的背景下量化可解释性的三个不同方面，即可解释性、可复制性和可再现性。