The topological information is essential for studying the relationship between nodes in a network. Recently, Network Representation Learning (NRL), which projects a network into a low-dimensional vector space, has been shown their advantages in analyzing large-scale networks. However, most existing NRL methods are designed to preserve the local topology of a network and they fail to capture the global topology. To tackle this issue, we propose a new NRL framework, named HSRL, to help existing NRL methods capture both local and global topological information of a network. Specifically, HSRL recursively compresses an input network into a series of smaller networks using a community-awareness compressing strategy. Then, an existing NRL method is used to learn node embeddings for each compressed network. Finally, the node embeddings of the input network are obtained by concatenating the node embeddings resulting from all compressed networks. Empirical studies of link prediction on five real-world datasets demonstrate the advantages of HSRL over state-of-the-art methods. © 2019 IEEE.
|Title of host publication
|Proceedings of the International Joint Conference on Neural Networks
|Institute of Electrical and Electronics Engineers Inc.
|Published - Jul 2019
Bibliographical noteThis work was supported by National Key R&D Program of China (Grant No. 2017YFC0804003), a 2018 IEEE Computational Intelligence Society Graduate Student Research Grant, the Program for Guangdong Introducing Innovative and Entrepreneurial Teams (Grant No. 2017ZT07X386), Shenzhen Peacock Plan (Grant No. KQTD2016112514355531), the Science and Technology Innovation Committee Foundation of Shenzhen (Grant Nos. ZDSYS201703031748284, JCYJ20170307105521943, JCYJ20170817112421757 and JCYJ20180504165652917) and the Program for University Key Laboratory of Guangdong Province (Grant No. 2017KSYS008).
- network topology
- Networks analysis
- representation learning