The Structure of Interdisciplinary Science: Uncovering and Explaining Roles in Citation Graphs

TL;DR

This paper introduces a new explainable framework for identifying and interpreting roles in large citation networks using small subgraph structures called graphlets, revealing interdisciplinary research patterns.

Contribution

It presents a novel method combining role discovery with explainability via graphlets, enhancing interpretability of roles in large, complex networks.

Findings

Identified key citation patterns reflecting interdisciplinary research

Demonstrated interpretability of roles in large citation graphs

Validated approach on a multidisciplinary citation network

Abstract

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