Influence Maximization
Information Diffusion
Social Network Analysis
Network Visualization
Visual Analytics

VAIM: Visual Analytics for Influence Maximization

The Influence Maximization (IM) problem entails selecting a seed set of users of a network that maximizes the influence spread, i.e., the expected number of users positively influenced by a stochastic diffusion process triggered by the seeds. Engineering and analyzing IM algorithms remains a difficult and demanding task due to the NP-hardness of the problem and the stochastic nature of the diffusion processes. Despite several heuristics being introduced, they often fail in providing enough information on how the network topology affects the diffusion process, precious insights that could help researchers improve their seed set selection. VAIM is a visual analytics system that supports users in analyzing, evaluating, and comparing information diffusion processes determined by different IM algorithms. See also a demonstration video.