Influence Maximization: Divide and Conquer

TL;DR

This paper presents a versatile framework for influence maximization that improves existing heuristics by dividing networks into sectors, enhancing influence spread detection especially in large, modular, and heterogeneous networks.

Contribution

The authors introduce a novel divide-and-conquer framework that enhances influence maximization methods by sector-based network division using various partitioning techniques.

Findings

Performance gain increases with network modularity and heterogeneity.

Framework scales linearly with network size, suitable for large networks.

Effective across real and synthetic network datasets.

Abstract

The problem of influence maximization, i.e., finding the set of nodes having maximal influence on a network, is of great importance for several applications. In the past two decades, many heuristic metrics to spot influencers have been proposed. Here, we introduce a framework to boost the performance of any such metric. The framework consists in dividing the network into sectors of influence, and then selecting the most influential nodes within these sectors. We explore three different methodologies to find sectors in a network: graph partitioning, graph hyperbolic embedding, and community structure. The framework is validated with a systematic analysis of real and synthetic networks. We show that the gain in performance generated by dividing a network into sectors before selecting the influential spreaders increases as the modularity and heterogeneity of the network increase. Also, we show that the division of the network into sectors can be efficiently performed in a time that scales linearly with the network size, thus making the framework applicable to large-scale influence maximization problems.