TY - GEN
T1 - Influential nodes in a one-wave diffusion model for location-based social networks
AU - Wu, Hao-Hsiang
AU - Yeh, Mi Yen
PY - 2013/12/1
Y1 - 2013/12/1
N2 - Taking the Foursquare data as an example, this paper investigates the problem of finding influential nodes in a location-based social network (LBSN). In Foursquare, people can share the location they visited and their opinions to others via the actions of checking in and writing tips. These check-ins and tips are likely to influence others on visiting the same places. To study the influence behavior in LBSNs, we first propose the attractiveness model to compute the influence probability among users. Then, we design a one-wave diffusion model, where we focus on the direct impact of the initially selected individuals on their first degree neighbors. Base on these two models, we propose algorithms to select the k influential nodes that maximize the influence spread in the complete-graph network and the network where only the links with friendship are preserved. We empirically show that the k influential nodes selected by our proposed methods have higher influence spread when compared to other methods.
AB - Taking the Foursquare data as an example, this paper investigates the problem of finding influential nodes in a location-based social network (LBSN). In Foursquare, people can share the location they visited and their opinions to others via the actions of checking in and writing tips. These check-ins and tips are likely to influence others on visiting the same places. To study the influence behavior in LBSNs, we first propose the attractiveness model to compute the influence probability among users. Then, we design a one-wave diffusion model, where we focus on the direct impact of the initially selected individuals on their first degree neighbors. Base on these two models, we propose algorithms to select the k influential nodes that maximize the influence spread in the complete-graph network and the network where only the links with friendship are preserved. We empirically show that the k influential nodes selected by our proposed methods have higher influence spread when compared to other methods.
UR - http://www.scopus.com/inward/record.url?scp=84893597929&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-37456-2_6
DO - 10.1007/978-3-642-37456-2_6
M3 - Conference contribution
AN - SCOPUS:84893597929
SN - 9783642374555
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 61
EP - 72
BT - Advances in Knowledge Discovery and Data Mining - 17th Pacific-Asia Conference, PAKDD 2013, Proceedings
Y2 - 14 April 2013 through 17 April 2013
ER -