VAIM: Visual Analytics for Influence Maximization
Alessio Arleo, Walter Didimo, Giuseppe Liotta, Silvia Miksch, and, Fabrizio Montecchiani
TL;DR
VAIM is a visual analytics tool designed to help users analyze, compare, and optimize influence maximization strategies in social networks through simulation and interactive analysis.
Contribution
This paper introduces VAIM, a novel visual analytics system that enables detailed analysis and improvement of influence maximization algorithms in large social networks.
Findings
Effective simulation of influence spread on large networks.
Enhanced understanding of seed set effectiveness.
Ability to iteratively improve influence strategies.
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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