Graph mining plays a pivotal role across a number of disciplines, and a variety of algorithms have been developed to answer who/what type questions. For example, what items shall we recommend to a given user on an e-commerce platform? The answers to such questions are typically returned in the form of a ranked list, and graph-based ranking methods are widely used in industrial information retrieval settings. However, these ranking algorithms have a variety of sensitivities, and even small changes in rank can lead to vast reductions in product sales and page hits. As such, there is a need for tools and methods that can help model developers and analysts explore the sensitivities of graph ranking algorithms with respect to perturbations within the graph structure. In this paper, we present a visual analytics framework for explaining and exploring the sensitivity of any graph-based ranking algorithm by performing perturbation-based what-if analysis. We demonstrate our framework through three case studies inspecting the sensitivity of two classic graph-based ranking algorithms (PageRank and HITS) as applied to rankings in political news media and social networks.

中文翻译：

---

使用可视化分析审核基于图的排名的敏感性

图挖掘在许多学科中发挥着关键作用，并且已经开发了各种算法来回答谁/什么类型的问题。例如，我们应该向电子商务平台上的特定用户推荐哪些商品？此类问题的答案通常以排名列表的形式返回，基于图的排名方法广泛用于工业信息检索设置。但是，这些排名算法具有多种敏感性，即使排名的微小变化也会导致产品销量和页面点击量的大幅下降。因此，需要能够帮助模型开发人员和分析人员探索图排序算法对图结构内扰动的敏感性的工具和方法。在本文中，我们提出了一个可视化分析框架，通过执行基于扰动的假设分析来解释和探索任何基于图的排名算法的敏感性。我们通过三个案例研究展示了我们的框架，这些案例研究检查了两种经典的基于图的排名算法（PageRank 和 HITS）应用于政治新闻媒体和社交网络排名的敏感性。