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Recently, how to expand data transmission to reduce cell data and repeatedcell transmission has received more and more research attention. In mobilesocial networks, content popularity prediction has always been an importantpart of traffic offloading and expanding data dissemination. However, currentmainstream content popularity prediction methods only use the number ofdownloads and shares or the distribution of user interests, which do notconsider important time and geographic location information in mobile socialnetworks, 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 basedon LSTM, which is designed to predict a complete D2D diffusion path. Our workis the first attempt in the world to use real data of MSN to predict diffusionpath with deep neural networks which conforms to the D2D structure. Compared tolinear sequence networks, only learn users' social features without timedistribution or GPS distribution and files' content features, our model canpredict the propagation path more accurately (up to 85.858\%) and can reachconvergence faster (less than 100 steps) because of the neural network thatconforms to the D2D structure and combines user social features and filesfeatures. Moreover, we can simulate generating a D2D propagation tree. Afterexperiment and comparison, it is found to be very similar to the ground-truthtrees. Finally, we define a user prototype refinement that can more accuratelydescribe the propagation sharing habits of a prototype user (including contentpreferences, time preferences, and geographic location preferences), andexperimentally validate the predictions when the user prototype is added to1000 classes, it is almost identical to the 50 categories.

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