Data-Driven Computational Social Science: A Survey

Zhang, Jun; Wang, Wei; Xia, Feng; Lin, Yu‐Ru; Tong, Hanghang

doi:10.1016/j.bdr.2020.100145

Cited by 56 publications

(25 citation statements)

References 168 publications

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“…Graph learning methods pave the way for graph analysis in the new representation space, and many graph analytical tasks, such as link prediction, recommendation and classification, can be solved efficiently [10], [11]. Graphical network representation sheds light on various aspects of social life, such as communication patterns, community structure, and information diffusion [12], [13]. According to the attributes of vertices, edges and subgraph, graph learning tasks can be divided into three categories, which are vertices based, edges based, and subgraph based, respectively.…”

Section: A What Is Graph Learning?mentioning

confidence: 99%

Graph Learning: A Survey

Xia

Sun

et al. 2021

IEEE Trans. Artif. Intell.

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300

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Graphs are widely used as a popular representation of the network structure of connected data. Graph data can be found in a broad spectrum of application domains such as social systems, ecosystems, biological networks, knowledge graphs, and information systems. With the continuous penetration of artificial intelligence technologies, graph learning (i.e., machine learning on graphs) is gaining attention from both researchers and practitioners. Graph learning proves effective for many tasks, such as classification, link prediction, and matching. Generally, graph learning methods extract relevant features of graphs by taking advantage of machine learning algorithms. In this survey, we present a comprehensive overview on the state-of-the-art of graph learning. Special attention is paid to four categories of existing graph learning methods, including graph signal processing, matrix factorization, random walk, and deep learning. Major models and algorithms under these categories are reviewed respectively. We examine graph learning applications in areas such as text, images, science, knowledge graphs, and combinatorial optimization. In addition, we discuss several promising research directions in this field.

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Section: A What Is Graph Learning?mentioning

confidence: 99%

Graph Learning: A Survey

Xia

Sun

et al. 2021

IEEE Trans. Artif. Intell.

Self Cite

300

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“…Currently, a large number of researchers have concentrated their efforts on such an emerging area with the technology of big data, yielding impressive results (Bai et al, 2020;Hou et al, 2020;Zhang et al, 2020;Xia et al, 2021). A systematic review is urgently needed to sort out the results and challenges of current research and to provide references for educational policymaking and subsequent research.…”

Section: Introductionmentioning

confidence: 99%

Educational Anomaly Analytics: Features, Methods, and Challenges

et al. 2022

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Anomalies in education affect the personal careers of students and universities' retention rates. Understanding the laws behind educational anomalies promotes the development of individual students and improves the overall quality of education. However, the inaccessibility of educational data hinders the development of the field. Previous research in this field used questionnaires, which are time- and cost-consuming and hardly applicable to large-scale student cohorts. With the popularity of educational management systems and the rise of online education during the prevalence of COVID-19, a large amount of educational data is available online and offline, providing an unprecedented opportunity to explore educational anomalies from a data-driven perspective. As an emerging field, educational anomaly analytics rapidly attracts scholars from a variety of fields, including education, psychology, sociology, and computer science. This paper intends to provide a comprehensive review of data-driven analytics of educational anomalies from a methodological standpoint. We focus on the following five types of research that received the most attention: course failure prediction, dropout prediction, mental health problems detection, prediction of difficulty in graduation, and prediction of difficulty in employment. Then, we discuss the challenges of current related research. This study aims to provide references for educational policymaking while promoting the development of educational anomaly analytics as a growing field.

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“…Data-driven decision making is the process of using evidences and insights derived from data to guide the decision making process and to verify a plan of actions before it is committed [26,98]. It plays a vital role in the success of businesses, industries, governments, and even an individual's life.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Decision Making in COVID-19 Response: A Survey

Yu¹,

Qing²,

Zhang³

et al. 2022

Preprint

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COVID-19 has spread all over the world, having an enormous effect on our daily life and work. In response to the epidemic, a lot of important decisions need to be taken to save communities and economies worldwide. Data clearly plays a vital role in effective decision making. Data-driven decision making uses data related evidence and insights to guide the decision making process and to verify the plan of action before it is committed. To better handle the epidemic, governments and policy making institutes have investigated abundant data originating from COVID-19. These data include those related to medicine, knowledge, media, etc. Based on these data, many prevention and control policies are made. In this survey paper, we summarize the progress of data-driven decision making in the response to COVID-19, including COVID-19 prevention and control, psychological counselling, financial aid, work resumption, and school re-opening. We also propose some current challenges and open issues in data-driven decision making, including data collection and quality, complex data analysis, and fairness in decision making. This survey paper sheds light on current policy making driven by data, which also provides a feasible direction for further scientific research.

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Data-Driven Computational Social Science: A Survey

Cited by 56 publications

References 168 publications

Graph Learning: A Survey

Graph Learning: A Survey

Educational Anomaly Analytics: Features, Methods, and Challenges

Data-Driven Decision Making in COVID-19 Response: A Survey

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