Network Inference and Influence Maximization from Samples

TL;DR

This paper addresses influence maximization from cascade samples in social networks, proposing novel algorithms for network inference and influence maximization that require weaker assumptions and provide constant approximation guarantees.

Contribution

It introduces new algorithms for influence maximization directly from cascade data and a network inference method that operates under milder assumptions than prior approaches.

Findings

Achieves constant approximation for influence maximization from cascade samples.

Develops network inference algorithms that do not rely on maximum-likelihood or convex programming.

Provides theoretical guarantees under mild conditions.

Abstract

Influence maximization is the task of selecting a small number of seed nodes in a social network to maximize the influence spread from these seeds. It has been widely investigated in the past two decades. In the canonical setting, the social network and its diffusion parameters are given as input. In this paper, we consider the more realistic sampling setting where the network is unknown and we only have a set of passively observed cascades that record the sets of activated nodes at each diffusion step. We study the task of influence maximization from these cascade samples (IMS) and present constant approximation algorithms for it under mild conditions on the seed set distribution. To achieve the optimization goal, we also provide a novel solution to the network inference problem, that is, learning diffusion parameters and the network structure from the cascade data. Compared with prior solutions, our network inference algorithms require weaker assumptions and do not rely on maximum-likelihood estimation and convex programming. Our IMS algorithms enhance the learning-and-then-optimization approach by allowing a constant approximation ratio even when the diffusion parameters are hard to learn, and we do not need any assumption related to the network structure or diffusion parameters.