Contributions to Proceedings:

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", issued by: David Auber and Pavel Valtr Eds.; Springer LNCS, Cham, 2020, ISBN: 978-3-030-68766-3, 115 - 123.

English abstract:
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.

Influence Maximization,Information Diffusion, Visual Analytics

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Electronic version of the publication:

Created from the Publication Database of the Vienna University of Technology.