Arleo, A., Didimo, W., Liotta, G., Miksch, S., & Montecchiani, F. (2020). VAIM: Visual Analytics for Influence Maximization. In Lecture Notes in Computer Science (pp. 115–123). Springer LNCS. https://doi.org/10.1007/978-3-030-68766-3_9
Visual Analytics; Influence Maximization; Information Diffusion
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.
Visual Computing and Human-Centered Technology: 100%