Explainability in Graph Data Science: Interpretability, replicability, and reproducibility of community detection
Explainability in Graph Data Science: Interpretability, replicability, and reproducibility of community detection
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DOI：
10.1109/msp.2022.3149471
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发表时间：
2022-07
影响因子：
14.9
通讯作者：
Selin Aviyente;Abdullah Karaaslanli
中科院分区：
文献类型：
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作者：
Selin Aviyente;Abdullah Karaaslanli
在许多现代数据科学问题中，数据由图(网络)表示，例如，社会、生物和通信网络。在过去的十年中，许多信号处理和机器学习(ML)算法被引入分析图结构数据。随着人们对图和基于图的学习任务在各种应用中的兴趣的增长，有必要探索图数据科学中的可解释性。在本文中，我们旨在探讨可解释图数据科学的问题，重点放在最基本的学习任务之一-社区发现上，因为它通常是从图中提取信息的第一步。社区是与特定功能相对应的较大网络中的密集子网络。尽管不同的社区检测方法在具有强模块化结构的合成网络上取得了成功，但这些算法应用于具有未知模块结构的真实网络时，其输出的质量和意义仍是未知的。受可解释人工智能(AI)和最大似然法(ML)最新进展的启发，本文从网络科学的角度提出了在社区检测的背景下对可解释性的三个不同方面进行量化的方法和度量标准，即可解释性、可复制性和可再现性。
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.