Influencing Busy People in a Social Network

TL;DR

This paper develops a new model for influence maximization in resource-constrained social networks, proposing efficient heuristics that significantly improve influence spread and resource utilization for viral marketing.

Contribution

It introduces a novel collective behavior model considering individual intent and resource limits, and provides scalable heuristics with proven effectiveness for influence maximization.

Findings

Heuristics increase resource utilization by 15-51% over naive methods.

The influence maximization problem is NP-hard but submodular, allowing approximation.

Proposed methods perform well on synthetic and real-world networks.

Abstract

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.