Abstract The existing network representation methods and their related variants are focused on preserving network topology structure or minimizing reconstruction error. However, data distribution of latent codes is ignored. To solve the problem, an adversarial graph convolutional networks(AGCN) is proposed. AGCN combines graph structure information and node attribute information to improve network representation learning performance, and enforces the latent codes to match a prior distribution. Moreover, an end-to-end multi-task learning framework(MTL) based on AGCN is introduced. It can perform link prediction and node classification simultaneously. The experiment shows that MTL achieves a good performance.
Fund:Supported by National Natural Science Foundation of China(No.61972135,61602159), Natural Science Foundation of Hei longjiang Province ( No. F201430 ), Fundamental Research
Funds of Universities in Heilongjiang Province(No. HDJCCX-201608,KJCX201815,KJCX201816), Innovation Talents Project of Science and Technology Bureau of Harbin(No.2017RAQXJ094,2017RAQXJ131)
Corresponding Authors:
LIU Yong, Ph.D., associate professor. His research interests include data mining and database.
About author:: CHEN Mengxue, master student. Her research interests include link prediction.
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2019, Vol. 32 
