VAIM: Visual Analytics for Influence Maximization
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.
Cite
@article{arxiv.2008.08821,
title = {VAIM: Visual Analytics for Influence Maximization},
author = {Alessio Arleo and Walter Didimo and Giuseppe Liotta and Silvia Miksch and Fabrizio Montecchiani},
journal= {arXiv preprint arXiv:2008.08821},
year = {2020}
}
Comments
Appears in the Proceedings of the 28th International Symposium on Graph Drawing and Network Visualization (GD 2020)