Scalable Influence Maximization for Multiple Products in Continuous-Time Diffusion Networks

TL;DR

This paper introduces a scalable, influence maximization algorithm for multiple products in continuous-time social networks, accounting for realistic constraints like limited user attention and budgets, and demonstrates its effectiveness on large networks.

Contribution

It formulates the influence maximization problem with multiple constraints as a submodular maximization task and develops a scalable randomized algorithm with provable approximation guarantees.

Findings

Achieves state-of-the-art effectiveness in influence maximization

Demonstrates scalability on networks with millions of nodes

Provides theoretical guarantees for influence estimation accuracy

Abstract

A typical viral marketing model identifies influential users in a social network to maximize a single product adoption assuming unlimited user attention, campaign budgets, and time. In reality, multiple products need campaigns, users have limited attention, convincing users incurs costs, and advertisers have limited budgets and expect the adoptions to be maximized soon. Facing these user, monetary, and timing constraints, we formulate the problem as a submodular maximization task in a continuous-time diffusion model under the intersection of a matroid and multiple knapsack constraints. We propose a randomized algorithm estimating the user influence in a network ($|\mathcal{V}|$ nodes, $|\mathcal{E}|$ edges) to an accuracy of $\epsilon$ with $n=\mathcal{O}(1/\epsilon^2)$ randomizations and $\tilde{\mathcal{O}}(n|\mathcal{E}|+n|\mathcal{V}|)$ computations. By exploiting the influence estimation algorithm as a subroutine, we develop an adaptive threshold greedy algorithm achieving an approximation factor $k_a/(2+2 k)$ of the optimal when $k_a$ out of the $k$ knapsack constraints are active. Extensive experiments on networks of millions of nodes demonstrate that the proposed algorithms achieve the state-of-the-art in terms of effectiveness and scalability.