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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.

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

http://dx.doi.org/10.1007/978-3-030-68766-3_9

https://publik.tuwien.ac.at/files/publik_290147.pdf