Temporal Community Analysis Using Centrality Measures

This research explores temporal community detection to identify patterns in course grading and offerings at The University of Illinois from Spring 2010 to Spring 2020. Using a dataset that includes course grades, subject codes, instructors, and student enrolment, the study applies four community det...

Full description

Saved in:
Bibliographic Details
Published in2024 IEEE Conference on Engineering Informatics (ICEI) pp. 1 - 8
Main Authors Rajeshkumar, Deepak, Jaysankar, Pranav, Yookeesh, M.R Sree Padma, Chandrasekaran, Balaji, Girijakumari, Smrithy
Format Conference Proceeding
LanguageEnglish
Published IEEE 20.11.2024
Subjects
Centrality Measures
Clustering algorithms
Community Detection
Cosine Similarity
Density measurement
Edge Density
Fluctuations
GPA
Greedy Modularity
Image edge detection
Laiden
Louvaine
Modularity
Overlap
Periodic structures
Persistence
Resource management
Size measurement
Springs
Stability analysis
Time measurement
Walktrap
Online AccessGet full text

Cover

More Information
Summary:This research explores temporal community detection to identify patterns in course grading and offerings at The University of Illinois from Spring 2010 to Spring 2020. Using a dataset that includes course grades, subject codes, instructors, and student enrolment, the study applies four community detection algorithms-Greedy Modularity, Louvain, Leiden, and Walktrap-to examine how courses group based on grading similarities over time. The data is pre-processed, split into time slices, and represented as graphs where nodes are courses and edges are weighted by cosine similarity of grading patterns. Key metrics such as modularity, community size, edge density, overlap, and persistence are used to evaluate algorithm performance. The results provide insights into the evolution of grading structures, course groupings, and instructional trends, offering a deeper understanding of the dynamics in educational data. This analysis contributes to improved decision-making in academic program development and resource allocation.
DOI:10.1109/ICEI64305.2024.10912264