Cost-aware Targeted Viral Marketing: Approximation with Less Samples

TL;DR

This paper introduces a more sample-efficient approximation algorithm for Cost-aware Targeted Viral Marketing, reducing the number of samples needed while maintaining theoretical guarantees, and demonstrating superior performance on real social networks.

Contribution

It presents a novel algorithm that uses fewer samples for influence maximization with cost-awareness, improving efficiency without sacrificing accuracy.

Findings

Outperforms state-of-the-art in sample efficiency

Reduces running time in experiments

Maintains theoretical approximation guarantees

Abstract

Cost-aware Targeted Viral Marketing (CTVM), a generalization of Influence Maximization (IM), has received a lot of attentions recently due to its commercial values. Previous approximation algorithms for this problem required a large number of samples to ensure approximate guarantee. In this paper, we propose an efficient approximation algorithm which uses fewer samples but provides the same theoretical guarantees based on generating and using important samples in its operation. Experiments on real social networks show that our proposed method outperforms the state-of-the-art algorithm which provides the same approximation ratio in terms of the number of required samples and running time.