Search

The traditional Graph Embedding algorithm and graph neural network model only use the attribute information or feature information of the nodes themselves when classifying the nodes in the network, but rarely use the structural information of the nodes in the network. It is also a problem worthy of further study to introduce the node network structure information to improve the classification accuracy when aggregating the graph neural network. Therefore, based on the GraphSage model, a new aggregation function was designed and a new GraphSage-Degree model was proposed on the node degree and node importance in the network. Firstly, the model obtained the importance of the nodes in the neighbourhood based on the node degree, then aggregated the features of the nodes relied on the importance, so that the important nodes in the network can aggregate as much feature information as possible, and set a hyperparameter D related to the node degree in the GraphSage-Degree, which can be adjusted to achieve the best classification state on different data sets.The GraphSage-Degree was tested on three publicly available datasets, Cora, Citeseer and Pubmed. The average improvement values of macro-F1 compared respectively with other methods are 8.72%, 10.37% and 8.29%, with the maximum improvement value of 38.84% on Pubmed. The average improvement values of micro-F1 are 8.97%, 11.16%, and 6.9%, respectively, with a maximum boost value of 38.39% on Pubmed. [ABSTRACT FROM AUTHOR]