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 graph structures when reducing nodes to dense vector representations. 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, we propose surrogate explanation for role discovery, a new framework for interpreting role assignments on large graphs using small subgraph structures known as graphlets. We demonstrate our framework on a small synthetic graph with prescribed structure, before applying them to a larger real-world network. In the second case, a large, multidisciplinary citation network, we successfully identify a number of important citation patterns or structures which reflect interdisciplinary research.

角色发现是将图上的节点集划分为结构相似的角色的类的任务。现代角色发现策略通常依赖于图嵌入技术，该技术能够在将节点减少为密集向量表示时识别复杂的图结构。然而，在使用大型现实网络时，很难解释或验证根据这些方法识别的一组角色。在这项工作中，受可解释人工智能领域进步的推动，我们提出了角色发现的替代解释，这是一种使用称为图基的小子图结构解释大图上的角色分配的新框架。我们在具有规定结构的小型合成图上演示了我们的框架，然后将它们应用到更大的现实世界网络中。在第二个案例中，一个大型的多学科引用网络，我们成功地识别了许多反映跨学科研究的重要引用模式或结构。