SINE: Scalable Incomplete Network Embedding

Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

24 Citations (Scopus)

Abstract

Attributed network embedding aims to learn low-dimensional vector representations for nodes in a network, where each node contains rich attributes/features describing node content. Because network topology structure and node attributes often exhibit high correlation, incorporating node attribute proximity into network embedding is beneficial for learning good vector representations. In reality, large-scale networks often have incomplete/missing node content or linkages, yet existing attributed network embedding algorithms all operate under the assumption that networks are complete. Thus, their performance is vulnerable to missing data and suffers from poor scalability. In this paper, we propose a Scalable Incomplete Network Embedding (SINE) algorithm for learning node representations from incomplete graphs. SINE formulates a probabilistic learning framework that separately models pairs of node-context and node-attribute relationships. Different from existing attributed network embedding algorithms, SINE provides greater flexibility to make the best of useful information and mitigate negative effects of missing information on representation learning. A stochastic gradient descent based online algorithm is derived to learn node representations, allowing SINE to scale up to large-scale networks with high learning efficiency. We evaluate the effectiveness and efficiency of SINE through extensive experiments on real-world networks. Experimental results confirm that SINE outperforms state-of-the-art baselines in various tasks, including node classification, node clustering, and link prediction, under settings with missing links and node attributes. SINE is also shown to be scalable and efficient on large-scale networks with millions of nodes/edges and high-dimensional node features. The source code of this paper is available at https://github.com/daokunzhang/SINE.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Data Mining, ICDM 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages737-746
Number of pages10
ISBN (Electronic)9781538691588
DOIs
Publication statusPublished - 27 Dec 2018
Externally publishedYes
Event18th IEEE International Conference on Data Mining, ICDM 2018 - Singapore, Singapore
Duration: 17 Nov 201820 Nov 2018

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume2018-November
ISSN (Print)1550-4786

Conference

Conference18th IEEE International Conference on Data Mining, ICDM 2018
Country/TerritorySingapore
CitySingapore
Period17/11/1820/11/18

Keywords

  • Incomplete network
  • Large scale
  • Network embeddding

ASJC Scopus subject areas

  • General Engineering

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