Modeling city locations as complex networks: An initial study

Lu Zhou, Yang Zhang, Jun Pang, Cheng Te Li

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Analyzing data collected from location-based social networks can reveal complex structure in human social relations. It can also lead to deep understandings of human mobility and help characterize city locations and their connectivity. In this paper, we construct location networks for six cities using a large-scale Instagram dataset. We find that these location networks share many topological features as in other different types of networks, along with properties specific to their cities. By mapping locations to their geographical coordinates, we further show that (1) our construction method can effectively reveal popular city locations, and (2) for two locations there is no clear correlation between their network distance and geographical distance. Moreover, all six location networks contain three or four large communities covering almost all locations in a city and the large communities in each city often exhibit clear spatial differences in geographical space.

Original languageEnglish
Pages (from-to)735-747
Number of pages13
JournalStudies in Computational Intelligence
Volume693
DOIs
Publication statusPublished - 2017 Jan 1

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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