Develops low-adaptivity framework and sketching techniques yielding nearly-linear time algorithms with guarantees for influence maximization on observed cascades, plus an improved tail bound for the independent cascade model.
An experimental study of the small world problem, pages 130--148
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Efficient Algorithms for Influence Maximization in General Models and Observed Cascades
Develops low-adaptivity framework and sketching techniques yielding nearly-linear time algorithms with guarantees for influence maximization on observed cascades, plus an improved tail bound for the independent cascade model.