The existent node classification methods based on network structure only pay attention to local network connection relationship. For obtaining wider network information, this paper developed a semi-supervised node classification algorithm(CBGN) based on geometric neighbor structure information. This algorithm improved random walk strategy with penalty factor in network to obtain arbitrary length node sequence for each node. It input these node sequences into the word2vec model for transforming the potential information into node vectors. CBGN combined gradient descent method and the coordinate descent method to optimize the SVM classification model. This method compared with current methods on four standard datasets. The results verify that the proposed algorithm improves the classification accuracy and has better classification effect.

[2020-09-05] Printed Article /doc/2019.03.0110/v2