Reconstructing spatial information diffusion networks with heterogeneous agents and text contents

Xinyue Ye, Wenbo Wang, Xiaoqi Zhang, Zhenlong Li, Dantong Yu, Jiaxin Du, Zhihui Chen

Research output: Contribution to journalArticlepeer-review

Abstract

It is important to reconstruct the hidden network structure from the infection status change of an information propagation process for evidence-based spatial decision-making. Unlike previous work, we not only consider the heterogeneity of the propagation agents, but also incorporate the heterogeneity of the text contents of information within the propagation process. In addition, the infection status is no longer restricted to the binary type (infected or not), and we allow the number of pieces of information texts to be counted which represents the degree of infection. The resulting model is a network-based multivariate recurrent event model, in which the interactions between different types of text, between different agents, between agents and text types, and their mutual impacts on the whole propagation process can be comprehensively investigated. On that basis, a nonparametric mean-field equation is derived to govern the propagation process, and a compressive sensing algorithm is provided to infer the hidden spatial propagation network from the infection status data. Finally, the proposed methodology is tested through synthetic data and a real data set of information diffusion on Twitter.

Original languageEnglish (US)
Pages (from-to)1654-1673
Number of pages20
JournalTransactions in GIS
Volume25
Issue number4
DOIs
StatePublished - Aug 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Earth and Planetary Sciences(all)

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