Efficient Algorithms for Influence Maximization in General Models and Observed Cascades
This work provides faster algorithms with theoretical guarantees for influence maximization, a key problem in social network analysis, benefiting practitioners who need scalable solutions.
The authors develop efficient algorithms for influence maximization in general stochastic models and observed cascades, achieving nearly-linear running time with provable guarantees and improving sample complexity by replacing a factor n with τ (number of diffusion steps) in the independent cascade model. Experiments show substantial speedups while maintaining solution quality.
We study influence maximization in general stochastic models, the observed cascades model, and the independent cascade (IC) model. For general stochastic models with only black-box sample access, we introduce a low-adaptivity optimization framework that improves sample complexity and running time over Sadeh et al. (2020) and is instrumental to all our results. We further introduce an adaptive algorithm guided by empirical variance, avoiding pessimistic worst-case bounds. Combining our optimization framework with sketching, we obtain the first algorithm with provable guarantees and nearly-linear running time for influence maximization on observed cascades, optimal up to logarithmic factors. For IC, we prove a novel tail bound replacing a factor $n$ with $τ$ (the number of diffusion steps) in sample complexity, improving over prior work when $τ$ is small, as is common due to small-world phenomena. Experiments confirm substantial speedups while maintaining solution quality.