Influence at Scale: Distributed Computation of Complex Contagion in Networks

Brendan Lucier, Joel Oren, Yaron Singer

Abstract: We consider the task of evaluating the spread of influence in large networks in the well-studied independent cascade model. We describe a novel sampling approach that can be used to design scalable algorithms with provable performance guarantees. These algorithms can be implemented in distributed computation frameworks such as MapReduce. We complement these results with a lower bound on the query complexity of influence estimation in this model. We validate the performance of these algorithms through experiments that demonstrate the efficacy of our methods and related heuristics.