Improving network embedding with partially available vertex and edge content

Lin Lan, Pinghui Wang, Junzhou Zhao, Jing Tao, John C.S. Lui, Xiaohong Guan

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Network embedding aims to learn a low-dimensional representation for each vertex in a network, which has recently shown its power in many graph mining problems such as vertex classification and link prediction. Most existing methods learn such representations according to network structure information, and some methods further consider vertex content in a network. Unlike prior works, we study the problem of network embedding with two distinctive properties: (1) content information exists on both vertices and edges; (2) only a part of vertices and edges have content information. To solve this problem, we propose a novel Partially available Vertex and Edge Content Boosted network embedding method, namely PVECB, which uses available vertex and edge content information to fine-tune structure-only representations through two hand-designed mechanisms respectively. Empirical results on four real-world datasets demonstrate that our method can effectively boost structure-only representations to capture more accurate proximities between vertices.
Original languageEnglish (US)
Pages (from-to)935-951
Number of pages17
JournalInformation Sciences
StatePublished - Dec 2 2019
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-06-21
Acknowledgements: The research presented in this paper is supported in part by Shenzhen Basic Research Grant (JCYJ20170816100819428), National Key R&D Program of China (2018YFC0830500), National Natural Science Foundation of China (61922067, U1736205, 61603290), Natural Science Basic Research Plan in Shaanxi Province of China (2019JM-159), and Natural Science Basic Research Plan in Zhejiang Province of China (LGG18F020016).

ASJC Scopus subject areas

  • Artificial Intelligence
  • Theoretical Computer Science
  • Software
  • Information Systems and Management
  • Control and Systems Engineering
  • Computer Science Applications


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