A framework for predicting scientific disruption based on graph signal processing

IF 7.4 1区 管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Houqiang Yu, Yian Liang
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引用次数: 0

Abstract

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.

基于图信号处理的科学干扰预测框架
识别科学混乱一直被认为是一项挑战,而预测科学混乱更是如此。我们认为,无法整合多维信息以及现有方法的可扩展性有限阻碍了更好的预测。本文开发了一个基于图信号处理(GSP)的框架来预测科学干扰,在基准数据集上实现了约 80% 的平均 AUC,平均超出先前方法 13.6% 的性能。该框架是统一的,可适应任何类型的信息,并具有可扩展性,有可能利用 GSP 技术进一步增强。该框架的直觉是:科学颠覆的特点是导致科学进化的巨大变化,而科学进化被认为是一个由图表示的复杂系统,而 GSP 是一种专门分析图结构数据的技术;因此,我们认为 GSP 非常适合科学进化建模和预测颠覆。在此框架基础上,我们开始进行破坏预测。内容、上下文和(引用)结构信息分别被定义为图信号。这些图信号的总变化是主要的预测指标,可以衡量进化幅度。为了说明我们框架的统一性和可扩展性,我们将以前很少考虑的 altmetrics 数据(论文的在线提及)定义为图信号,并使用另一个指标--图信号的分散熵(衡量科学进化的混乱程度)--分别进行预测。我们的框架还具有可解释性强的优势,有助于更好地理解科学混乱现象。分析表明,科学中断不仅会导致知识内容的巨大变化,还会导致上下文(如期刊和作者)的巨大变化,并将导致后续演化的混乱。最后,还提出了基于该框架的干扰预测的几个实用的未来方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Information Processing & Management
Information Processing & Management 工程技术-计算机:信息系统
CiteScore
17.00
自引率
11.60%
发文量
276
审稿时长
39 days
期刊介绍: Information Processing and Management is dedicated to publishing cutting-edge original research at the convergence of computing and information science. Our scope encompasses theory, methods, and applications across various domains, including advertising, business, health, information science, information technology marketing, and social computing. We aim to cater to the interests of both primary researchers and practitioners by offering an effective platform for the timely dissemination of advanced and topical issues in this interdisciplinary field. The journal places particular emphasis on original research articles, research survey articles, research method articles, and articles addressing critical applications of research. Join us in advancing knowledge and innovation at the intersection of computing and information science.
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