BCIM: Budget and capacity constrained influence maximization in multilayer networks

TL;DR

This paper introduces BCIM, a new influence maximization framework in multilayer networks that considers budget and capacity constraints, using a genetic algorithm to improve influence spread efficiency.

Contribution

It proposes the BCIM problem and a novel MMGA algorithm to effectively optimize influence maximization under multiple real-world constraints.

Findings

MMGA improves influence spreading by at least 10% over baselines.

The framework effectively handles multilayer networks with budget and capacity constraints.

Experiments on synthetic and real networks validate the approach's efficiency.

Abstract

Influence maximization (IM) seeks to identify a seed set that maximizes influence within a network, with applications in areas such as viral marketing, disease control, and political campaigns. The budgeted influence maximization (BIM) problem extends IM by incorporating cost constraints for different nodes. However, the current BIM problem, limited by budget alone, often results in the selection of numerous low-cost nodes, which may not be applicable to real-world scenarios. Moreover, considering that users can transmit information across multiple social platforms, solving the BIM problem across these platforms could lead to more optimized resource utilization. To address these challenges, we propose the Budget and Capacity Constrained Influence Maximization (BCIM) problem within multilayer networks and introduce a Multilayer Multi-population Genetic Algorithm (MMGA) to solve it. The MMGA employs modules, such as initialization, repair, and parallel evolution, designed not only to meet budget and capacity constraints but also to significantly enhance algorithmic efficiency. Extensive experiments on both synthetic and empirical multilayer networks demonstrate that MMGA improves spreading performance by at least 10% under the two constraints compared to baselines extended from classical IM problems. The BCIM framework introduces a novel direction in influence maximization, providing an effective and efficient solution to the problem.