Cross-Layer Design of Influence Maximization in Mobile Social Networks

TL;DR

This paper proposes a cross-layer strategy for influence maximization in mobile social networks that reduces message overhead by selecting representative agents, addressing the inefficiency of traditional centralized algorithms in distributed settings.

Contribution

It introduces the Agent Selection Problem (ASP), proves its NP-hardness, and develops a distributed algorithm to efficiently select agents, reducing message overhead in mobile social networks.

Findings

Significant reduction in message overhead compared to existing methods.

Effective distributed algorithm for agent selection.

Validated results on real and synthetic datasets.

Abstract

Most prior algorithms for influence maximization focused are designed for Online Social Networks (OSNs) and require centralized computation. Directly deploying the above algorithms in distributed Mobile Social Networks (MSNs) will overwhelm the networks due to an enormous number of messages required for seed selection. In this paper, therefore, we design a new cross-layer strategy to jointly examine MSN and mobile ad hoc networks (MANETs) to facilitate efficient seed selection, by extracting a subset of nodes as agents to represent nearby friends during the distributed computation. Specifically, we formulate a new optimization problem, named Agent Selection Problem (ASP), to minimize the message overhead transmitted in MANET. We prove that ASP is NP-Hard and design an effectively distributed algorithm. Simulation results in real and synthetic datasets manifest that the message overhead can be significantly reduced compared with the existing approaches.