D2D-LSTM based Prediction of the D2D Diffusion Path in Mobile Social Networks

TL;DR

This paper introduces D2D-LSTM, a deep neural network model that predicts D2D diffusion paths in mobile social networks using real MSN data, incorporating user social, temporal, and geographic features for improved accuracy.

Contribution

First to use MSN data with deep neural networks to predict D2D diffusion paths, integrating social, temporal, and geographic features for enhanced prediction accuracy.

Findings

Predicts diffusion paths with up to 85.858% accuracy.

Converges in less than 100 steps, faster than linear models.

Generates D2D propagation trees similar to ground-truth data.

Abstract

Recently, how to expand data transmission to reduce cell data and repeated cell transmission has received more and more research attention. In mobile social networks, content popularity prediction has always been an important part of traffic offloading and expanding data dissemination. However, current mainstream content popularity prediction methods only use the number of downloads and shares or the distribution of user interests, which do not consider important time and geographic location information in mobile social networks, and all of data is from OSN which is not same as MSN. In this work, we propose D2D Long Short-Term Memory (D2D-LSTM), a deep neural network based on LSTM, which is designed to predict a complete D2D diffusion path. Our work is the first attempt in the world to use real data of MSN to predict diffusion path with deep neural networks which conforms to the D2D structure. Compared to linear sequence networks, only learn users' social features without time distribution or GPS distribution and files' content features, our model can predict the propagation path more accurately (up to 85.858\%) and can reach convergence faster (less than 100 steps) because of the neural network that conforms to the D2D structure and combines user social features and files features. Moreover, we can simulate generating a D2D propagation tree. After experiment and comparison, it is found to be very similar to the ground-truth trees. Finally, we define a user prototype refinement that can more accurately describe the propagation sharing habits of a prototype user (including content preferences, time preferences, and geographic location preferences), and experimentally validate the predictions when the user prototype is added to 1000 classes, it is almost identical to the 50 categories.