Ying, Z.T., You, J.X., Morris, C., Ren, X., Hamilton, W. and Leskovec, J. (2018) Hierarchical Graph Representation Learning with Differentiable Pooling. ArXiv: 1806.08804.

has been cited by the following article:

• TITLE: Research on Node Classification Based on Joint Weighted Node Vectors

  AUTHORS: Li Dai

  KEYWORDS: Node Classification, Network Embedding, Representation Learning, Weighted Vectors Training

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.12 No.1, January 30, 2024

  ABSTRACT: Node of network has lots of information, such as topology, text and label information. Therefore, node classification is an open issue. Recently, one vector of node is directly connected at the end of another vector. However, this method actually obtains the performance by extending dimensions and considering that the text and structural information are one-to-one, which is obviously unreasonable. Regarding this issue, a method by weighting vectors is proposed in this paper. Three methods, negative logarithm, modulus and sigmoid function are used to weight-trained vectors, then recombine the weighted vectors and put them into the SVM classifier for evaluation output. By comparing three different weighting methods, the results showed that using negative logarithm weighting achieved better results than the other two using modulus and sigmoid function weighting, and was superior to directly concatenating vectors in the same dimension.