Influence Maximization in Social Network Considering Memory Effect and Social Reinforcement Effect

Wang Wang,Liu Liu,Zhu Zhu

doi:10.3390/fi11040095

Wang Wang, Liu Liu + Show 1 more

Open Access

https://doi.org/10.3390/fi11040095

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Abstract

Social networks have attracted a lot of attention as novel information or advertisement diffusion media for viral marketing. Influence maximization describes the problem of finding a small subset of seed nodes in a social network that could maximize the spread of influence. A lot of algorithms have been proposed to solve this problem. Recently, in order to achieve more realistic viral marketing scenarios, some constrained versions of influence maximization, which consider time constraints, budget constraints and so on, have been proposed. However, none of them considers the memory effect and the social reinforcement effect, which are ubiquitous properties of social networks. In this paper, we define a new constrained version of the influence maximization problem that captures the social reinforcement and memory effects. We first propose a novel propagation model to capture the dynamics of the memory and social reinforcement effects. Then, we modify two baseline algorithms and design a new algorithm to solve the problem under the model. Experiments show that our algorithm achieves the best performance with relatively low time complexity. We also demonstrate that the new version captures some important properties of viral marketing in social networks, such as such as social reinforcements, and could explain some phenomena that cannot be explained by existing influence maximization problem definitions.

Highlights

The study of the spreading process on social networks has attracted a lot of attention recently for its great practical value in word-of-mouth marketing or viral marketing [1,2]
Li et al [22] argued that, in more real-world cases, marketers usually target certain products at particular groups of customers. They proposed the labeled influence maximization problem, which aims to find a set of seed nodes which can trigger the maximum spread of influence on the target customers in a labeled social network
Given a social network modeled as an indirect graph G = (V, E, PE), a specific propagation model called “M” and a small number called “k”, the influence maximization problem is to find a initial set called “S” with k nodes in the graph such that under the propagation model M, the expected number of nodes influenced by S is as large as possible

Summary

Introduction

The study of the spreading process on social networks has attracted a lot of attention recently for its great practical value in word-of-mouth marketing or viral marketing [1,2]. Domingos et al [3] and Kempe et al [4] first treated the viral marketing problem as an influence maximization problem, which chooses a limited number of initial nodes to spread information of a product in a social network such that the resulting number of customers that bought the product is maximized. They discarded some important properties of viral marketing in order to simplify the problem.

Related Work

Traditional Influence Maximization Problem

Dependent Cascade Model for Viral Marketing

Influence Maximization under DCM4VM

Algorithms

General Greedy Algorithm

Experimental Parameters