Combining Traditional Marketing and Viral Marketing with Amphibious Influence Maximization

TL;DR

This paper introduces the Amphibious Influence Maximization (AIM) model that integrates traditional and viral marketing in social networks, providing complexity analysis and approximation algorithms under certain assumptions.

Contribution

The paper formulates the AIM model combining content provider influence and social network influence, proves its NP-hardness, and offers approximation algorithms under low-rank influence matrices.

Findings

AIM problem is NP-hard to approximate within any constant factor.

Constant rank assumption enables a polynomial-time approximation algorithm.

Results extend to models with monotone submodular influence functions.

Abstract

In this paper, we propose the amphibious influence maximization (AIM) model that combines traditional marketing via content providers and viral marketing to consumers in social networks in a single framework. In AIM, a set of content providers and consumers form a bipartite network while consumers also form their social network, and influence propagates from the content providers to consumers and among consumers in the social network following the independent cascade model. An advertiser needs to select a subset of seed content providers and a subset of seed consumers, such that the influence from the seed providers passing through the seed consumers could reach a large number of consumers in the social network in expectation. We prove that the AIM problem is NP-hard to approximate to within any constant factor via a reduction from Feige's k-prover proof system for 3-SAT5. We also give evidence that even when the social network graph is trivial (i.e. has no edges), a polynomial time constant factor approximation for AIM is unlikely. However, when we assume that the weighted bi-adjacency matrix that describes the influence of content providers on consumers is of constant rank, a common assumption often used in recommender systems, we provide a polynomial-time algorithm that achieves approximation ratio of $(1-1/e-\epsilon)^3$ for any (polynomially small) $\epsilon > 0$. Our algorithmic results still hold for a more general model where cascades in social network follow a general monotone and submodular function.