Reconstructing spatial information diffusion networks with heterogeneous agents and text contents
Document Type
Article
Publication Date
8-1-2021
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.
Identifier
85104950152 (Scopus)
Publication Title
Transactions in GIS
External Full Text Location
https://doi.org/10.1111/tgis.12747
e-ISSN
14679671
ISSN
13611682
First Page
1654
Last Page
1673
Issue
4
Volume
25
Grant
20YJC790176
Fund Ref
National Science Foundation
Recommended Citation
Ye, Xinyue; Wang, Wenbo; Zhang, Xiaoqi; Li, Zhenlong; Yu, Dantong; Du, Jiaxin; and Chen, Zhihui, "Reconstructing spatial information diffusion networks with heterogeneous agents and text contents" (2021). Faculty Publications. 3923.
https://digitalcommons.njit.edu/fac_pubs/3923
