Deep Learning Approach to Link Weight Prediction

Deep learning has been successful in various domains including image recognition, speech recognition and natural language processing. However, the research on its application in graph mining is still in an early stage. Here we present Model R, a neural network model created to provide a deep learning approach to link weight prediction problem. This model extracts knowledge of nodes from known links' weights and uses this knowledge to predict unknown links' weights. We demonstrate the power of Model R through experiments and compare it with stochastic block model and its derivatives. Model R shows that deep learning can be successfully applied to link weight prediction and it outperforms stochastic block model and its derivatives by up to 73% in terms of prediction accuracy. We anticipate this new approach to provide effective solutions to more graph mining tasks.