Degree-biased random walk for large-scale network embedding

Yunyi Zhang, Zhan Shi, Dan Feng, Xiuxiu Zhan

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)
2 Downloads (Pure)

Abstract

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.

Original languageEnglish
Pages (from-to)198-209
Number of pages12
JournalFuture Generation Computer Systems
Volume100
DOIs
Publication statusPublished - 2019

Keywords

  • Network embedding
  • Random walks
  • Scale-free

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