Restore the Original data by Embedded data

Abstract

Network is an important representation method to describe related objects. For network, the most core research is to reasonably represent the characteristic information of nodes in the network, which is called network representation learning. In recent years, many scholars have proposed many excellent representation learning algorithms, through which the original network data can be embedded in low-dimensional representation, which can help us classify the nodes in the network, and the nodes can also be used as point coordinates in Euclidean space for visualization. Existing algorithms are all aimed at embedding the original data, but how to use the embedded data to restore the original data when the original data is incomplete has not been studied by scholars. In order to solve this problem, this paper proposes two solutions of deep learning, one is the artificial neural network method based on deep learning, and the other is the attention mechanism method based on deep learning. The experimental results of this paper show that these two methods are very effective.