  We identify influential early adopters in a social network, where individuals
are resource constrained, to maximize the spread of multiple, costly behaviors.
A solution to this problem is especially important for viral marketing. The
problem of maximizing influence in a social network is challenging since it is
computationally intractable. We make three contributions. First, propose a new
model of collective behavior that incorporates individual intent, knowledge of
neighbors actions and resource constraints. Second, we show that the multiple
behavior influence maximization is NP-hard. Furthermore, we show that the
problem is submodular, implying the existence of a greedy solution that
approximates the optimal solution to within a constant. However, since the
greedy algorithm is expensive for large networks, we propose efficient
heuristics to identify the influential individuals, including heuristics to
assign behaviors to the different early adopters. We test our approach on
synthetic and real-world topologies with excellent results. We evaluate the
effectiveness under three metrics: unique number of participants, total number
of active behaviors and network resource utilization. Our heuristics produce
15-51% increase in expected resource utilization over the naive approach.
