Network Embedding Algorithm Taking in Variational Graph AutoEncoder

Dongming Chen, Mingshuo Nie, Hupo Zhang, Zhen Wang and Dongqi Wang
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Dongming Chen: Software College, Northeastern University, Shenyang 110169, China
Mingshuo Nie: Software College, Northeastern University, Shenyang 110169, China
Hupo Zhang: Software College, Northeastern University, Shenyang 110169, China
Zhen Wang: Software College, Northeastern University, Shenyang 110169, China
Dongqi Wang: Software College, Northeastern University, Shenyang 110169, China

Mathematics, 2022, vol. 10, issue 3, 1-13

Abstract: Complex networks with node attribute information are employed to represent complex relationships between objects. Research of attributed network embedding fuses the topology and the node attribute information of the attributed network in the common latent representation space, to encode the high-dimensional sparse network information to the low-dimensional dense vector representation, effectively improving the performance of the network analysis tasks. The current research on attributed network embedding is presently facing problems of high-dimensional sparsity of attribute eigenmatrix and underutilization of attribute information. In this paper, we propose a network embedding algorithm taking in a variational graph autoencoder (NEAT-VGA). This algorithm first pre-processes the attribute features, i.e., the attribute feature learning of the network nodes. Then, the feature learning matrix and the adjacency matrix of the network are fed into the variational graph autoencoder algorithm to obtain the Gaussian distribution of the potential vectors, which more easily generate high-quality node embedding representation vectors. Then, the embedding of the nodes obtained by sampling this Gaussian distribution is reconstructed with structural and attribute losses. The loss function is minimized by iterative training until the low-dimension vector representation, containing network structure information and attribute information of nodes, can be better obtained, and the performance of the algorithm is evaluated by link prediction experimental results.

Keywords: attributed network; network embedding; random walk; autoencoder (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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