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Contributions to Proceedings:

A. Arleo, W. Didimo, G. Liotta, S. Miksch, F. Montecchiani:
"VAIM: Visual Analytics for Influence Maximization";
accepted for publication in: "28th International Symposium on Graph Drawing and Network Visualization", issued by: David Auber and Pavel Valtr Eds.; Springer LNCS.



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.

Keywords:
Influence Maximization,Information Diffusion, Visual Analytics


Electronic version of the publication:
https://publik.tuwien.ac.at/files/publik_290147.pdf


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