TipTop: (Almost) Exact Solutions for Influence Maximization in Billion-scale Networks

TL;DR

TipTop is a scalable algorithm that provides near-exact solutions for influence maximization in billion-scale networks, enabling precise benchmarking and addressing the NP-hardness of the problem.

Contribution

The paper introduces TipTop, a novel method that achieves near-optimal solutions for large-scale influence maximization by reducing sampling and using Integer Programming.

Findings

TipTop scales to billion-scale networks like Twitter.

It provides $(1-psilon)$-optimal solutions.

It enables benchmarking against the true optimal influence.

Abstract

In this paper, we study the Cost-aware Target Viral Marketing (CTVM) problem, a generalization of Influence Maximization (IM). CTVM asks for the most cost-effective users to influence the most relevant users. In contrast to the vast literature, we attempt to offer exact solutions. As the problem is NP-hard, thus, exact solutions are intractable, we propose TipTop, a $(1-\epsilon)$-optimal solution for arbitrary $\epsilon>0$ that scales to very large networks such as Twitter. At the heart of TipTop lies an innovative technique that reduces the number of samples as much as possible. This allows us to exactly solve CTVM on a much smaller space of generated samples using Integer Programming. Furthermore, TipTop lends a tool for researchers to benchmark their solutions against the optimal one in large-scale networks, which is currently not available.