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We consider here information spread which propagates with certain probabilityfrom nodes just activated to their not yet activated neighbors. Diffusioncascades can be triggered by activation of even a small set of nodes. Suchactivation is commonly performed in a single stage. A novel approach based onsequential seeding is analyzed here resulting in three fundamentalcontributions. First, we propose a coordinated execution of randomized choicesto enable precise comparison of different algorithms in general. We apply ithere when the newly activated nodes at each stage of spreading attempt toactivate their neighbors. Then, we present a formal proof that sequentialseeding delivers at least as large coverage as the single stage seeding does.Moreover, we also show that, under modest assumptions, sequential seedingachieves coverage provably better than the single stage based approach usingthe same number of seeds and node ranking. Finally, we present experimentalresults showing how single stage and sequential approaches on directed andundirected graphs compare to the well-known greedy approach to provide theobjective measure of the sequential seeding benefits. Surprisingly, applyingsequential seeding to a simple degree-based selection leads to higher coveragethan achieved by the computationally expensive greedy approach currentlyconsidered to be the best heuristic.

Probing Limits of Information Spread with Sequential Seeding