Link Prediction Approach for Opportunistic Networks Based on Recurrent Neural Network

Abstract

The target of link prediction is used to estimate the possibility of future links among nodes through known network structures and nodes information. According to the time-varying characteristics of the opportunistic network, the historical information of node pairs has a significant influence on the future connection state. We propose a novel link prediction approach which is based on the recurrent neural network link prediction (RNN-LP) framework. With the help of time series method, we define the vector that is made up of the node information and historical connection information of the node pairs, in which a sequence vector is constructed. Benefiting from RNN in sequence modeling, the time domain characteristics were extracted in the process of the dynamic evolution of the opportunistic network. Hence, the future link prediction becomes significantly better. By utilizing iMote traces Cambridge and MIT reality datasets, experimental results are obtained to reveal that RNN-LP method gives better accuracy and stability than the prediction techniques of the common neighbor, Adamic-Adar, resource allocation, local path, and Katz.