2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDMLSP-30.2
Paper Title Fast Hierarchy Preserving Graph Embedding via Subspace Constraints
Authors Xu Chen, Peking University, China; Lun Du, Microsoft Research, China; Mengyuan Chen, Beijing Normal University, China; Yun Wang, QingQing Long, Kunqing Xie, Peking University, China
SessionMLSP-30: Graph Signal Processing
LocationGather.Town
Session Time:Thursday, 10 June, 14:00 - 14:45
Presentation Time:Thursday, 10 June, 14:00 - 14:45
Presentation Poster
Topic Machine Learning for Signal Processing: [MLR-SSUP] Self-supervised and semi-supervised learning
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Hierarchy preserving network embedding is a method that project nodes into feature space by preserving the hierarchy property of networks. Recently, researches on network representation have considerably profited from taking hierarchy into consideration. Among these works, SpaceNE stands out by preserving hierarchy with the help of subspace constraints on the hierarchy subspace system. However, like all other hierarchy preserving network embedding methods, SpaceNE is time-consuming and cannot generalize to new nodes. In this paper, we propose an inductive method, FastHGE, to learn node representations more efficiently and generalize to new nodes more easily. Empirically, the experiment of node classification demonstrates that the convergence speed of FastHGE is increased by 30 times in the case of the same accuracy with SpaceNE.