The Importance of Communities for Learning to Influence

TL;DR

This paper explores influence maximization in social networks, highlighting the importance of community structure, and introduces a heuristic that performs well in practice and has theoretical guarantees on community-structured graphs.

Contribution

It presents a simple heuristic leveraging community structure to improve influence maximization, with proven guarantees on stochastic block model graphs.

Findings

Heuristic performs well experimentally on real social networks.

Algorithm achieves constant factor approximation on stochastic block model graphs.

Community structure is crucial for effective influence maximization.

Abstract

We consider the canonical problem of influence maximization in social networks. Since the seminal work of Kempe, Kleinberg, and Tardos, there have been two largely disjoint efforts on this problem. The first studies the problem associated with learning the parameters of the generative influence model. The second focuses on the algorithmic challenge of identifying a set of influencers, assuming the parameters of the generative model are known. Recent results on learning and optimization imply that in general, if the generative model is not known but rather learned from training data, no algorithm can yield a constant factor approximation guarantee using polynomially-many samples, drawn from any distribution. In this paper, we design a simple heuristic that overcomes this negative result in practice by leveraging the strong community structure of social networks. Although in general the approximation guarantee of our algorithm is necessarily unbounded, we show that this algorithm performs well experimentally. To justify its performance, we prove our algorithm obtains a constant factor approximation guarantee on graphs generated through the stochastic block model, traditionally used to model networks with community structure.