VAIM: Visual Analytics for Influence Maximization
This provides a tool for researchers and practitioners to better understand and optimize influence spread in social networks, though it is incremental as it builds on existing IM algorithms.
The paper tackles the influence maximization problem in social networks by introducing VAIM, a visual analytics system that allows users to simulate, analyze, and modify seed sets to improve influence spread, with results demonstrated through interactive simulations on large networks.
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.