Graph representation learning or graph embedding is a classical topic in data mining. Current embedding methods are mostly non-parametric, where all the embedding points are unconstrained free points in the target space. These approaches suffer from limited scalability and an over-flexible representation. In this paper, we propose a parametric graph embedding by fusing graph topology information and node content information. The embedding points are obtained through a highly flexible non-linear transformation from node content features to the target space. This transformation is learned using the contrastive loss function of the siamese network to preserve node adjacency in the input graph. On several benchmark network datasets, the proposed GraPASA method shows a significant margin over state-of-the-art techniques on benchmark graph representation tasks.
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): Award No. 2639
Acknowledgements: This work was partially supported and funded by King Abdullah University of Science and Technology (KAUST) through the KAUST Office of Sponsored Research (OSR) under Award No. 2639, National Key R&D Program of China (2017YFC0803700), National Natural Science Foundation of China (61502320), Science Foundation of Shenzhen City in China (JCYJ20160419152942010), the State Key Laboratory of Software Development Environment, and Aeronautical Science Foundation of China.