Approximation Algorithms for Coordinating Ad Campaigns on Social Networks

We study a natural model of coordinated social ad campaigns over a social network, based on models of Datta et al. and Aslay et al. Multiple advertisers are willing to pay the host - up to a known budget - per user exposure, whether the exposure is sponsored or orgain (i.e. shared by a friend). Campaigns are seeded with sponsored ads to some users, but no user must be exposed to too many sponsored ads. Thus, while ad campaigns proceed independently over the network, they need to be carefully coordinated with respect to their seed sets. We study the objective of maximizing host's total ad revenue. Our main result is to show that under a broad class of influence models, the problem can be reduced to maximizing a submodular function subject to two matroid constraints; it can therefore be approximated within a factor essentially 1/2 in polynomial time. When there is no bound on the individual seed set sizes of advertisers, the constraints correspond only to a single matroid, and the guarantee can be improved to 1-1/e; in that case, a factor 1/2 is achieved by a practical greedy algorithm. The 1-1/e approximation algorithm for matroid-constrained problem is far from practical; however, we show that specifically under the Independent Cascade model, LP rounding and Reverse Reachability techniques can be combined to obtain a 1-1/e approximation algorithm. Our theoretical results are complemented by experiments evaluating the extent to which the coordination of multiple ad campaigns inhibits the revenue obtained from each individual campaign, as a function of the similarity of the influence networks and strength of ties in the networks. Our experiments suggest that as networks for different advertisers become less similar, the harmful effect of competition decreases. With respect to tie strengths, we show that the most harm is done in an intermediate range.