Propagation Measure on Circulation Graphs for Tourism Behavior Analysis

EasyChair Preprint 8889

Abstract

Social network analysis has widespread in recent years, especially in digital tourism. Indeed the large amount of data that tourists produce during their travels represents an effective source to understand their behavior and is of great importance for tourism stakeholders. This paper studies the propagation effect of tourists on the territory thanks to geotagged circulation graphs. Those graphs reflect traffic flows which need to be analyzed over time and space. A new weighted measure is introduced for circulation characterization based on both topologies and distances. This measure helps to determine the behavior of tourists on local and global areas. An optimization strategy based on spanning trees is applied to reduce the computation on the whole graph while keeping a good approximation of the behavior. The approach is simulated on various graphs and evaluated experimentaly over a real dataset at various geographic zones, scales, communities, and time.

Keyphrases: digital tourism, graph data mining, spanning trees

@booklet{EasyChair:8889,
  author    = {Hugo Prevoteau and Sonia Djebali and Zhao Laiping and Nicolas Travers},
  title     = {Propagation Measure on Circulation Graphs for Tourism Behavior Analysis},
  howpublished = {EasyChair Preprint 8889},
  year      = {EasyChair, 2022}}