Beiträge in Tagungsbänden:
A. Arleo, W. Didimo, G. Liotta, S. Miksch, F. Montecchiani:
"VAIM: Visual Analytics for Influence Maximization";
in: "28th International Symposium on Graph Drawing and Network Visualization",
herausgegeben von: David Auber and Pavel Valtr Eds.;
Springer LNCS,
Cham,
2020,
ISBN: 978-3-030-68766-3,
S. 115
- 123.
Kurzfassung englisch:
In social networks, individuals' decisions are strongly influenced by recommendations from their friends and acquaintances. The influence maximization (IM) problem asks to select a seed set of users that maximizes the influence spread, i.e., the expected number of users influenced through a stochastic diffusion process triggered by the seeds. In this paper, we present VAIM, a visual analytics system that supports users in analyzing the information diffusion process determined by different IM algorithms. By using VAIM one can: (i) simulate the information spread for a given seed set on a large network, (ii) analyze and compare the effectiveness of different seed sets, and (iii) modify the seed sets to improve the corresponding influence spread.
Schlagworte:
Influence Maximization,Information Diffusion, Visual Analytics
"Offizielle" elektronische Version der Publikation (entsprechend ihrem Digital Object Identifier - DOI)
http://dx.doi.org/10.1007/978-3-030-68766-3_9
Elektronische Version der Publikation:
https://publik.tuwien.ac.at/files/publik_290147.pdf
Erstellt aus der Publikationsdatenbank der Technischen Universität Wien.