论文标题
跨学科科学的结构：揭示和解释引文图中的作用
The Structure of Interdisciplinary Science: Uncovering and Explaining Roles in Citation Graphs
论文作者
论文摘要
角色发现是将图表上的一组节点划分为结构相似角色类别的任务。现代角色发现的策略通常依赖于图形嵌入技术，这些技术能够识别复杂的局部结构。但是，在使用大型现实世界网络时，很难根据这些方法来解释或验证一组角色。在这项工作中，是由可解释的人工智能（XAI）领域进步的动机，我们提出了一个新的框架，用于使用称为Graphlets的小型子图结构在大图上解释角色分配。我们在大型多学科的引文网络上演示了我们的方法，我们成功地确定了许多重要的引用模式，这些模式反映了跨学科研究
Role discovery is the task of dividing the set of nodes on a graph into classes of structurally similar roles. Modern strategies for role discovery typically rely on graph embedding techniques, which are capable of recognising complex local structures. However, when working with large, real-world networks, it is difficult to interpret or validate a set of roles identified according to these methods. In this work, motivated by advancements in the field of explainable artificial intelligence (XAI), we propose a new framework for interpreting role assignments on large graphs using small subgraph structures known as graphlets. We demonstrate our methods on a large, multidisciplinary citation network, where we successfully identify a number of important citation patterns which reflect interdisciplinary research
