A framework for predicting scientific disruption based on graph signal processing

Identifying scientific disruption is consistently recognized as challenging, and more so is to predict it. We suggest that better predictions are hindered by the inability to integrate multidimensional information and the limited scalability of existing methods. This paper develops a framework based on graph signal processing (GSP) to predict scientific disruption, achieving an average AUC of about 80 % on benchmark datasets, surpassing the performance of prior methods by 13.6 % on average. The framework is unified, adaptable to any type of information, and scalable, with the potential for further enhancements using technologies from GSP. The intuition of this framework is: scientific disruption is characterized by leading to dramatic changes in scientific evolution, which is recognized as a complex system represented by a graph, and GSP is a technique that specializes in analyzing data on graph structures; thus, we argue that GSP is well-suited for modeling scientific evolution and predicting disruption. Based on this proposed framework, we proceed with disruption predictions. The content, context, and (citation) structure information is respectively defined as graph signals. The total variations of these graph signals, which measure the evolutionary amplitude, are the main predictors. To illustrate the unity and scalability of our framework, altmetrics data (online mentions of the paper) that seldom considered previously is defined as graph signal, and another indicator, the dispersion entropy of graph signal (measuring chaos of scientific evolution), is used for predicting respectively. Our framework also provides advantages of interpretability for a better understanding on scientific disruption. The analysis indicates that the scientific disruption not only results in dramatic changes in the knowledge content, but also in context (e.g., journals and authors), and will lead to chaos in subsequent evolution. At last, several practical future directions for disruption predictions based on the framework are proposed.