Adaptive Seeding in Social Networks

Abstract

Abstract-The algorithmic challenge of maximizing information diffusion through word-of-mouth processes in social networks has been heavily studied in the past decade. Despite immense progress and an impressive arsenal of techniques, the algorithmic framework makes idealized assumptions regarding access to the network that can often result in poor performance of state-of-the-art techniques.In this paper we introduce a new framework which we call Adaptive Seeding. The framework is a two-stage stochastic optimization model designed to leverage the high potential that typically lies in neighboring nodes of arbitrary samples of social networks. Our main result is an algorithm which is a constant factor approximation of the optimal adaptive policy for any influence function in the Triggering model.

Approximability of Adaptive Seeding under Knapsack Constraints

“…The Adaptive Seeding model was introduced in [25], motivated by the question of influence maximization in social networks formalized by Kempe, Kleinberg, and Tardos [16]. The problem considered was under cardinality constraints, and the main result applies to a strict subclass of submodular functions.…”

“…One seeks to select among certain available nodes in a network, and then, adaptively, among neighbors of those nodes as they become available, in order to maximize influence in the overall network. Despite recent strong approximation results [25,1], very little is known about the problem when nodes can take on different activation costs. Surprisingly, designing adaptive seeding algorithms that can appropriately incentivize users with heterogeneous activation costs introduces fundamental challenges that do not exist in the simplified version of the problem.…”

“…In the past, only the unweighted case of the problem (often called the case of cardinality constraints) has been considered, giving strong approximation guarantees for various classes of monotone submodular functions [25,1,14]. The weighted case where nodes can take on different costs to become activated captures a broad scope of applications, though is far more complex.…”

“…To overcome the problem of low degree nodes a new approach to information dissemination in social networks was recently suggested in [25]. Rather than spend the entire budget on the nodes in the sample, one can spend a fraction of the budget on the sample in order for them to attract their friends, wait for the friends to become accessible, and optimize influence on the friends with the remaining budget.…”

Adaptive Rumor Spreading

“…Already Domingos and Richardson [14] identify this issue and state that: A more sophisticated alternative would be to plan a marketing strategy by explicitly simulating the sequential adoption of a product by customers given different interventions at different times, and adapting the strategy as new data on customer response arrives. Along these lines, Seeman and Singer [24] consider a two stage extension of the Kempe et al model.…”