Improving approximate extraction of functional similar regions from large-scale spatial networks based on greedy selection of representative nodes of different areas

Improving approximate extraction of functional similar regions from large-scale spatial networks based on greedy selection of representative nodes of different areas

Abstract Dividing a geographical region into some subregions with common characteristics is an important research topic, and has been studied in many research fields such as urban planning and transportation planning. In this paper, by network analysis approach, we attempt to extract functionally si...

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Bibliographic Details
Main Authors: Takayasu Fushimi, Kazumi Saito, Tetsuo Ikeda, Kazuhiro Kazama
Format: Article
Language:English
Published: SpringerOpen 2018-07-01
Series:Applied Network Science
Subjects:
Spatial network
Functional similarity
Node clustering
Greedy algorithm
Transfer learning
Online Access:http://link.springer.com/article/10.1007/s41109-018-0075-2

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http://link.springer.com/article/10.1007/s41109-018-0075-2

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