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arXiv · 2008.08821

VAIM: Visual Analytics for Influence Maximization

Alessio Arleo·Walter Didimo·Giuseppe Liotta·Silvia Miksch·Fabrizio Montecchiani
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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.

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Alessio Arleo, Walter Didimo, Giuseppe Liotta, Silvia Miksch, Fabrizio Montecchiani. 2020-08-20. VAIM: Visual Analytics for Influence Maximization. https://arxiv.org/abs/2008.08821

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