Holistic Influence Maximization: Combining Scalability and Efficiency with Opinion-Aware Models

TL;DR

This paper presents a holistic approach to influence maximization in social networks by introducing an opinion-aware model and scalable algorithms that balance effectiveness with computational efficiency.

Contribution

It proposes a novel opinion-cum-interaction model and develops scalable heuristic algorithms, EaSyIM and OSIM, for influence maximization considering opinions.

Findings

Algorithms maintain within 5% deviation of best methods

Effective on large real-world datasets

Significantly improve scalability and efficiency

Abstract

The steady growth of graph data from social networks has resulted in wide-spread research in finding solutions to the influence maximization problem. In this paper, we propose a holistic solution to the influence maximization (IM) problem. (1) We introduce an opinion-cum-interaction (OI) model that closely mirrors the real-world scenarios. Under the OI model, we introduce a novel problem of Maximizing the Effective Opinion (MEO) of influenced users. We prove that the MEO problem is NP-hard and cannot be approximated within a constant ratio unless P=NP. (2) We propose a heuristic algorithm OSIM to efficiently solve the MEO problem. To better explain the OSIM heuristic, we first introduce EaSyIM - the opinion-oblivious version of OSIM, a scalable algorithm capable of running within practical compute times on commodity hardware. In addition to serving as a fundamental building block for OSIM, EaSyIM is capable of addressing the scalability aspect - memory consumption and running time, of the IM problem as well. Empirically, our algorithms are capable of maintaining the deviation in the spread always within 5% of the best known methods in the literature. In addition, our experiments show that both OSIM and EaSyIM are effective, efficient, scalable and significantly enhance the ability to analyze real datasets.