Title

Hierarchical Influence Maximization For Advertising In Multi-Agent Markets

Abstract

Maximizing product adoption within a customer social network under a constrained advertising budget is an important special case of the general influence maximization problem. Specialized optimization techniques that account for product correlations and community effects can outperform network-based techniques that do not model interactions that arise from marketing multiple products to the same consumer base. However, it can be infeasible to use exact optimization methods that utilize expensive matrix operations on larger networks without parallel computation techniques. In this paper, we present a hierarchical influence maximization approach for product marketing that constructs an abstraction hierarchy for scaling optimization techniques to larger networks. An exact solution is computed on smaller partitions of the network, and a candidate set of influential nodes is propagated upward to an abstract representation of the original network that maintains distance information. This process of abstraction, solution, and propagation is repeated until the resulting abstract network is small enough to be solved exactly. Our proposed method scales to much larger networks and outperforms other influence maximization techniques on marketing products. Copyright 2013 ACM.

Publication Date

1-1-2013

Publication Title

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013

Number of Pages

21-27

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/2492517.2492622

Socpus ID

84893242716 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84893242716

This document is currently not available here.

Share

COinS