  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

Social and Information Networks