Correction

This article refers to:

Simulating the spatial diffusion of memes on social media networks

Article title: Simulating the spatial diffusion of memes on social media networks

Authors: Dang, L.; Chen, Z.; Lee, J.; Tsou, M.; Ye, X.

Journal: International Journal of Geographical Information Science

Bibliometrics: Volume 33, Number 8, pages 1545–1568

DOI: http://dx.doi.org/10.1080/13658816.2019.1591414

When the above article was first published online, the following simulation algorithm were omitted.

Corrections can be found as follows.

Formally, the simulation algorithm is as below:

Initialization

(1) Set up N_s, N_o, Prob_opinion, Prob_normal, Stop_N

Select N_s seed nodes and put them into Set_seed,

Select N_o opinion leader nodes and put them into Set_opinion,

Set Set_normal = Set_nodes - Set_seed - Set_opinion.

Set Set_active = ∅, Set_lastactive = ∅, put all nodes into Set_inactive

Conduct community detection. Initialize observer agent.

(2) Node Classification

Seed nodes, opinion leader nodes, bridge nodes.

(3) Community Detection

Community_C, Number of communities

Community_CN, number of nodes in each community

Community_COE, number of opinion leader nodes in each community

Community_B, number of bridge nodes between communities, a bridge node is a node that connects two or more communities.

Behavioral Rules for information diffusion:

(1) At the first-time step, for each v ∈ Set_seed:

Set_active = Set_active ∪ {v}

Set_inactive = Set_inactive – {v}

Set_lastactive = Set_lastactive∪ {v}.

(2) At any regular time step, for each node u ∈ Set_lastactive, check all the inactive nodes that node v connected to. Supposed the link is (u,v), generate a random number r between 0 and 1.

if u ∈ Set_opinion and Prob_opinion < r,

Set_active = Set_active ∪ {v}

Set_inactive = Set_inactive – {v}

Set_lastactive = Set_lastactive∪ {v}

if u ∈ Set_normal and Prob_normal < r,

Set_active = Set_active ∪ {v}

Set_inactive = Set_inactive – {v}

Set_lastactive = Set_lastactive∪ {v}

if v ∈ Set_inactive and Prob_out < r,

Set_active = Set_active ∪ {v}

Set_inactive = Set_inactive – {v}

Set_lastactive = Set_lastactive∪ {v}

if v ∈ Set_active, Adoption_N += 1,

Set_lastactive = Set_lastactive – {U}

(3) Update observer agents:

Diffusion Process (Visualization process)

Propagate proportion (graph)

if Adoption_N= Adoption_N-1, stop the process of diffusion.

Otherwise execute the step (2) iteratively.

Taylor & Francis apologizes for these error(s).