Machine Learning Based Graph Mining of Large-scale Network and Optimization
Mingyue Liu · 2021
Network science possesses an unreplaceable status in solving social and scientific problems. This study focuses on investigating machine learning based graph mining of large-scale network and optimization by using the California road network dataset from Stanford as the large-scale social network. After building different neural networks with various hyperparameters and selected learning activation functions, accuracy results were compared and conclusions include: 1) Changing the activation functions does not have much effect on accuracy compared to neural network structures, 2) Changing the learning rate does not have much effect either and 3) exponential linear unit (ELU) is sensitive to the change of hidden layer size and kernel compared to rectifier linear unit (ReLU) and hyperbolic tangent (Tanh), causing decrement of accuracy after growth of hidden layer size brings overfitting. These conclusions were drawn based on numeric experiments where the final accuracy rate kept the average of every ten trials. However, given this specific dataset, the accuracy of all the models remain high so that varying neural network parameters or structures does not present much distinctive divergence.