关键词: 异质信息网络, 表示学习, 元路径, 神经网络, 网络嵌入

Abstract:

Metapath2vec and Metapath2vec++, the classical algorithm of heterogeneous information network representation learning, just maintain the original topological structure of the network without the characteristics of clustering structure，then lead to the decrease of the accuracy of node representation in networks. HINSC and HINSC++, two heterogeneous network representation learning models, are proposed based on the principle of meta-path random walk to preserves the clustering structure. Taking one-hot representation of the network nodes as input of the feedforward neural network, with the nonlinear transformation of the hidden layer, it preserves the node’s neighbor topological and clustering structure at the output layer, and learning the low dimensional representation of heterogeneous network nodes by using the random gradient descent algorithm. The experimental results on two real heterogeneous networks show that compared with the presentation of Metapath2vec and Metapath2vec++, the NMI values in clustering tasks with HINSC and HINSC++ are increased by 12.46%~26.22%, and the values of Macro-F1 and Micro-F1 are increased by 9.32%~17.24% in classification tasks.

Key words: heterogeneous information network, representation learning, meta-path, neural network, network embedding