Reconstructing spatial information diffusion networks with heterogeneous agents and text contents uri icon

abstract

  • AbstractIt is important to reconstruct the hidden network structure from the infection status change of an information propagation process for evidencebased spatial decisionmaking. 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 networkbased 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 meanfield 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.

published proceedings

  • TRANSACTIONS IN GIS

author list (cited authors)

  • Ye, X., Wang, W., Zhang, X., Li, Z., Yu, D., Du, J., & Chen, Z.

citation count

  • 3

complete list of authors

  • Ye, Xinyue||Wang, Wenbo||Zhang, Xiaoqi||Li, Zhenglong||Yu, Dantong||Du, Jiaxin||Chen, Zhihui

publication date

  • August 2021

publisher