Effector Detection in Social Networks

TL;DR

This paper introduces two novel algorithms for effector detection in social networks, one based on influence distance and the other on maximum likelihood estimation, demonstrating their effectiveness through simulations.

Contribution

It presents a new influence-distance-based approach with a 3-approximation and an MLE-based method for effector detection, including solutions for both acyclic and general graphs.

Findings

The influence-distance approach effectively identifies key effectors.

The MLE method finds optimal effectors in polynomial time for acyclic graphs.

Algorithms perform well in simulations on real-world social networks.

Abstract

In a social network, influence diffusion is the process of spreading innovations from user to user. An activation state identifies who are the active users who have adopted the target innovation. Given an activation state of a certain diffusion, effector detection aims to reveal the active users who are able to best explain the observed state. In this paper, we tackle the effector detection problem from two perspectives. The first approach is based on the influence distance that measures the chance that an active user can activate its neighbors. For a certain pair of users, the shorter the influence distance, the higher probability that one can activate the other. Given an activation state, the effectors are expected to have short influence distance to active users while long to inactive users. By this idea, we propose the influence-distance-based effector detection problem and provide a 3-approximation. Second, we address the effector detection problem by the maximum likelihood estimation (MLE) approach. We prove that the optimal MLE can be obtained in polynomial time for connected directed acyclic graphs. For general graphs, we first extract a directed acyclic subgraph that can well preserve the information in the original graph and then apply the MLE approach to the extracted subgraph to obtain the effectors. The effectiveness of our algorithms is experimentally verified via simulations on the real-world social network.