Abstract. We study the problem of maximizing the expected spread of an innovation or behavior within a social network, in the presence of "word-of-mouth" referral. Our work builds on the observation that individuals' decisions to purchase a product or adopt an innovation are strongly influenced by recommendations from their friends and acquaintances. Understanding and leveraging this influence may thus lead to a much larger spread of the innovation than the traditional view of marketing to individuals in isolation. In this paper, we define a natural and general model of influence propagation that we term the decreasing cascade model, generalizing models used in the sociology and economics communities. In this model, as in related ones, a behavior spreads in a cascading fashion according to a probabilistic rule, beginning with a set of initially "active" nodes. We study the target set selection problem: we wish to choose a set of individuals to target for initial activation, such that the cascade beginning with this active set is as large as possible in expectation. We show that in the decreasing cascade model, a natural greedy algorithm is a 1 − 1/e − ε approximation for selecting a target set of size k.
They showed the NP-hardness of the problem in both models, and showed that a natural greedy strategy has a (1 − 1/e − ǫ)-approximation guarantee in both models; these results were generalized to a more general cascade model in REF .
Influential Nodes in a Diffusion Model for Social Networks
ICALP
Computer Science