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We identify influential early adopters that achieve a target behaviordistribution for a resource constrained social network with multiple costlybehaviors. This problem is important for applications ranging from collectivebehavior change to corporate viral marketing campaigns. In this paper, wepropose a model of diffusion of multiple behaviors when individual participantshave resource constraints. Individuals adopt the set of behaviors that maximizetheir utility subject to available resources. We show that the problem ofinfluence maximization for multiple behaviors is NP-complete. Thus we proposeheuristics, which are based on node degree and expected immediate adoption, toselect early adopters. We evaluate the effectiveness under three metrics:unique number of participants, total number of active behaviors and networkresource utilization. We also propose heuristics to distribute the behaviorsamongst the early adopters to achieve a target distribution in the population.We test our approach on synthetic and real-world topologies with excellentresults. Our heuristics produce 15-51\% increase in resource utilization overthe na\"ive approach.

How Do We Find Early Adopters Who Will Guide a Resource Constrained Network Towards a Desired Distribution of Behaviors?