Journal
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
Volume 100, Issue -, Pages 198-209Publisher
ELSEVIER
DOI: 10.1016/j.future.2019.05.033
Keywords
Network embedding; Scale-free; Random walks
Categories
Funding
- NSFC, China [61772216, 61821003, U1705261]
- Wuhan Application Basic Research Project [2017010201010103]
- Fund from Science, Technology and Innovation Commission of Shenzhen Municipality [JCYJ20170307172248636]
- Fundamental Research Funds for the Central Universities, China
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Network embedding aims at learning node representation by preserving the network topology. Previous embedding methods do not scale for large real-world networks which usually contain millions of nodes. They generally adopt a one-size-fits-all strategy to collect information, resulting in a large amount of redundancy. In this paper, we propose DiaRW, a scalable network embedding method based on a degree-biased random walk with variable length to sample context information for learning. Our walk strategy can well adapt to the scale-free feature of real-world networks and extract information from them with much less redundancy. In addition, our method can greatly reduce the size of context information, which is efficient for large-scale network embedding. Empirical experiments on node classification and link prediction prove not only the effectiveness but also the efficiency of DiaRW on a variety of real-world networks. Our algorithm is able to learn the network representations with millions of nodes and edges in hours on a single machine, which is tenfold faster than previous methods. (C) 2019 Elsevier B.V. All rights reserved.
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