Maximizing Social Welfare in a Competitive Diffusion Model

TL;DR

This paper introduces a new utility-driven model for competitive influence maximization, addressing economic incentives and collective item effects, and proposes efficient algorithms with strong approximation guarantees.

Contribution

It develops a novel utility-driven propagation model for competitive influence maximization and provides approximation algorithms with proven performance guarantees.

Findings

Algorithms outperform baselines in solution quality

Algorithms run efficiently on large networks

Effective in both synthetic and real utility scenarios

Abstract

Influence maximization (IM) has garnered a lot of attention in the literature owing to applications such as viral marketing and infection containment. It aims to select a small number of seed users to adopt an item such that adoption propagates to a large number of users in the network. Competitive IM focuses on the propagation of competing items in the network. Existing works on competitive IM have several limitations. (1) They fail to incorporate economic incentives in users' decision making in item adoptions. (2) Majority of the works aim to maximize the adoption of one particular item, and ignore the collective role that different items play. (3) They focus mostly on one aspect of competition -- pure competition. To address these concerns we study competitive IM under a utility-driven propagation model called UIC, and study social welfare maximization. The problem in general is not only NP-hard but also NP-hard to approximate within any constant factor. We, therefore, devise instant dependent efficient approximation algorithms for the general case as well as a $(1-1/e-\epsilon)$-approximation algorithm for a restricted setting. Our algorithms outperform different baselines on competitive IM, both in terms of solution quality and running time on large real networks under both synthetic and real utility configurations.