A Study on Hybrid Hierarchical Network Representation Learning

Authors

DOI:

https://doi.org/10.13052/jwe1540-9589.20611

Keywords:

Network Representation Learning, Dimension Reduction, Graph Contraction.

Abstract

Network representation learning (NRL) aims to convert nodes of a network into vector forms in Euclidean space. The information of a network is needed to be preserved as much as possible when NRL converts nodes into vector representation. A hybrid approach proposed in this paper is a framework to improve other NRL methods by considering the structure of densely connected nodes (community-like structure). HARP [1] is to contract a network into a series of contracted networks and embed them from the high-level contracted network to the low-level one. The vector representation (or embedding) for a high-level contracted network is used to initialize the learning process of a low-level contracted graph hierarchically. In this method (Hybrid Approach), HARP is revised by using a well-designed initialization process on the most high-level contracted network to preserve more community-like structure information.

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Author Biography

Yongxiang Hu, Huanggang Normal University, Hubei, 438000, China

Yongxiang Hu is a Associate researcher of the Huanggang Normal University, Hubei, China. He received the B.S. and M.S. degrees in computer Engineering System from Huazhong University of Science and Technology, Hubei, China, in 2001 and 2008, His research focuses on information security, Network architecture and network security technology.

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Published

2021-10-18

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Articles