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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.<\/jats:p>","DOI":"10.1007\/s41109-023-00551-w","type":"journal-article","created":{"date-parts":[[2023,5,26]],"date-time":"2023-05-26T05:04:42Z","timestamp":1685077482000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Surrogate explanations for role discovery on graphs"],"prefix":"10.1007","volume":"8","author":[{"given":"Eoghan","family":"Cunningham","sequence":"first","affiliation":[]},{"given":"Derek","family":"Greene","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,26]]},"reference":[{"issue":"4","key":"551_CR1","doi-asserted-by":"publisher","first-page":"1182","DOI":"10.1016\/j.joi.2018.09.001","volume":"12","author":"G Abramo","year":"2018","unstructured":"Abramo G, D\u2019Angelo CA, Zhang L (2018) A comparison of two approaches for measuring interdisciplinary research output: the disciplinary diversity of authors vs the disciplinary diversity of the reference list. 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