Influence Maximization With Visual Analytics

In social networks, individuals’ decisions are strongly influenced by recommendations from their friends, acquaintances, and favorite renowned personalities. The popularity of online social networking platforms makes them the prime venues to advertise products and promote opinions. The <italic>Influence Maximization</italic> (IM) problem entails selecting a <italic>seed set</italic> of users 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. In this paper, we present VAIM, a visual analytics system that supports users in analyzing, evaluating, and comparing information diffusion processes determined by different IM algorithms. Furthermore, VAIM provides useful insights that the analyst can use to modify the seed set of an IM algorithm, so to improve its influence spread. We assess our system by: <inline-formula><tex-math notation="LaTeX">$(i)$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>(</mml:mo><mml:mi>i</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="arleo-ieq1-3190623.gif"/></alternatives></inline-formula> a qualitative evaluation based on a guided experiment with two domain experts on two different data sets; <inline-formula><tex-math notation="LaTeX">$(ii)$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>(</mml:mo><mml:mi>i</mml:mi><mml:mi>i</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="arleo-ieq2-3190623.gif"/></alternatives></inline-formula> a quantitative estimation of the value of the proposed visualization through the ICE-T methodology by Wall <italic>et al.</italic> (IEEE TVCG - 2018). The twofold assessment indicates that VAIM effectively supports our target users in the visual analysis of the performance of IM algorithms.