Mapping the Curricular Structure and Contents of Network Science Courses
Hiroki Sayama

TL;DR
This study analyzes 30 network science courses to map their curricular structures, revealing common topic clusters and typical content flows, which reflect consensus and variations in the field's teaching approaches.
Contribution
It introduces a novel method of representing course contents as a topic network and uncovers the typical curricular sequences and clusters in network science education.
Findings
Identified seven main topic clusters in network science courses.
Revealed typical curricular flow starting from basic examples to advanced topics.
Mapped consensus and variations in network science teaching approaches.
Abstract
As network science has matured as an established field of research, there are already a number of courses on this topic developed and offered at various higher education institutions, often at postgraduate levels. In those courses, instructors adopted different approaches with different focus areas and curricular designs. We collected information about 30 existing network science courses from various online sources, and analyzed the contents of their syllabi or course schedules. The topics and their curricular sequences were extracted from the course syllabi/schedules and represented as a directed weighted graph, which we call the topic network. Community detection in the topic network revealed seven topic clusters, which matched reasonably with the concept list previously generated by students and educators through the Network Literacy initiative. The minimum spanning tree of the topic…
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Taxonomy
TopicsComplex Network Analysis Techniques · Online Learning and Analytics · Bioinformatics and Genomic Networks
