| dc.description.abstract | With the increasing popularity of machine learning, more and more industries have introduced machine learning to assist the development of the industry, which has made it more integrated into our lives, and the corresponding required technologies have also emerged. However, the bottleneck that machine learning is bound to experience is the limitation of equipment resources when expands to more fields. In the case of convolutional neural networks, which is commonly used in graphic recognition tasks, uses the pictures as input data that can be freely scaled to the size required for training. However, for the social network, the image size of the social network graph is much larger than the general graphic data, and it is difficult to discard the data content in it, so it is impossible to use the general scaling technology, and it is impossible to use ordinary operations for machine learning training. We have proposed a system for use in dynamic social network analysis. With the hierarchical clustering algorithm that we proposed, and then using the multi-block partition method, finally combined with the
autoencoder, it becomes a composite compression technology formed. The best balance is achieved in ensuring that the data is not distorted and compression efficiency. Experiments show that our proposed method can greatly increase the amount of social network data that the neural network model can process, and can reduce the
computational burden of the prediction model, and can also reduce the degree of dependence on hardware devices. | en_US |