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A Comparison of Network Clustering Algorithms for Keywords Network Analysis

Authors: Youngho Lee*, Kyunghee University & University of West Georgia, Yubin Lee, Kyunghee University & University of West Georgia, Ana Stanescu, University of West Georgia, Chul Sue Hwang, Kyunghee University, Jeong Seong, University of West Georgia
Topics: Geographic Information Science and Systems
Keywords: network clustering, keywords network analysis, GIS
Session Type: Paper
Presentation File: No File Uploaded

Network analysis has been used in a variety of fields such as transportation, research trends. In analyzing a network, the detection of sub-clusters is very important to identify network characteristics. Many network clustering algorithms have been developed. However, it makes difficult to choose a clustering algorithm, when different algorithms bring different results. The goal of this research is to compare keywords network clustering algorithms to find out their characteristics in keywords network analysis. We used 12,055 keywords from 6,027 abstracts presented at the 2019 AAG conference, and compared nine widely-used network clustering algorithms – Edge betweenness, Fast greedy, Infomap, Label propagation, Leading eigenvector, Louvain, Optimal, Spin-glass, and Walktrap. This research will help researchers choose the optimal algorithm in keywords network analysis.

ACKNOWLEDGEMENT: This research was supported by the MSIT (Ministry of Science, ICT), Republic of Korea, under the High-Potential Individuals Global Training Program (IITP-2019-0-01603) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation).

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