Influence Maximization (IM) in Complex Networks with Limited Visibility Using Statistical Methods

TL;DR

This paper addresses influence maximization in social networks with limited visibility by integrating link prediction techniques, specifically using ERGM, to improve seed node selection in real-world, partially observable graphs.

Contribution

It introduces a novel approach combining influence maximization with link prediction via ERGM to handle limited visibility in social networks.

Findings

The proposed method is effective on real-world datasets.

Link prediction improves influence maximization accuracy.

The approach is computationally efficient for large networks.

Abstract

A social network (SN) is a social structure consisting of a group representing the interaction between them. SNs have recently been widely used and, subsequently, have become suitable and popular platforms for product promotion and information diffusion. People in an SN directly influence each other's interests and behavior. One of the most important problems in SNs is to find people who can have the maximum influence on other nodes in the network in a cascade manner if they are chosen as the seed nodes of a network diffusion scenario. Influential diffusers are people who, if they are chosen as the seed set in a publishing issue in the network, that network will have the most people who have learned about that diffused entity. This is a well-known problem in literature known as influence maximization (IM) problem. Although it has been proven that this is an NP-complete problem and does not have a solution in polynomial time, it has been argued that it has the properties of sub modular functions and, therefore, can be solved using a greedy algorithm. Most of the methods proposed to improve this complexity are based on the assumption that the entire graph is visible. However, this assumption does not hold for many real-world graphs. This study is conducted to extend current maximization methods with link prediction techniques to pseudo-visibility graphs. To this end, a graph generation method called the exponential random graph model (ERGM) is used for link prediction. The proposed method is tested using the data from the Snap dataset of Stanford University. According to the experimental tests, the proposed method is efficient on real-world graphs.