VAIM: Visual Analytics for Influence Maximization

Teaser Image
Author
Conference Paper
Editor
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.
Year of Publication
2020
Conference Name
28th International Symposium on Graph Drawing and Network Visualization
Publisher
Lecture Notes in Computer Science
Funding projects
Video Link
Supplementary Material
Download citation