Independent Asymmetric Embedding for Information Diffusion Prediction on Social Networks

TL;DR

This paper introduces an independent asymmetric embedding method for social network information diffusion prediction, leveraging heat diffusion principles and user co-occurrence regulation to enhance accuracy and efficiency.

Contribution

It proposes a novel embedding approach that models influence and susceptibility separately, incorporating heat diffusion kernels and user co-occurrence for improved prediction.

Findings

Achieves higher predictive accuracy on real-world datasets.

Demonstrates improved cost-effectiveness over existing methods.

Effectively captures user influence and susceptibility dynamics.

Abstract

The prediction for information diffusion on social networks has great practical significance in marketing and public opinion control. It aims to predict the individuals who will potentially repost the message on the social network. One type of method is based on demographics, complex networks and other prior knowledge to establish an interpretable model to simulate and predict the propagation process, while the other type of method is completely data-driven and maps the nodes to a latent space for propagation prediction. Existing latent space design and embedding methods lack consideration for the intervene among users. In this paper, we propose an independent asymmetric embedding method to embed each individual into one latent influence space and multiple latent susceptibility spaces. Based on the similarity between information diffusion and heat diffusion phenomenon, the heat diffusion kernel is exploited in our model and establishes the embedding rules. Furthermore, our method captures the co-occurrence regulation of user combinations in cascades to improve the calculating effectiveness. The results of extensive experiments conducted on real-world datasets verify both the predictive accuracy and cost-effectiveness of our approach.