No PANE, No Gain: Scaling Attributed Network Embedding in a Single Server

Renchi Yang, Jieming Shi, Xiaokui Xiao, Yin Yang, Sourav S. Bhowmick, Juncheng Liu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Given a graph G where each node is associated with a set of attributes, attributed network embedding (ANE) maps each node v 2 G to a compact vector Xv, which can be used in downstream machine learning tasks in a variety of applications. Existing ANE solutions do not scale to massive graphs due to prohibitive computation costs or generation of low-quality embeddings. This paper proposes PANE, an effective and scalable approach to ANE computation for massive graphs in a single server that achieves state-of-The-Art result quality on multiple benchmark datasets for two common prediction tasks: link prediction and node classification. Under the hood, PANE takes inspiration from well-established data management techniques to scale up ANE in a single server. Specifically, it exploits a carefully formulated problem based on a novel random walk model, a highly efficient solver, and non-Trivial parallelization by utilizing modern multi-core CPUs. Extensive experiments demonstrate that PANE consistently outperforms all existing methods in terms of result quality, while being orders of magnitude faster.

Original languageEnglish
Pages (from-to)42-49
Number of pages8
JournalSIGMOD Record
Volume51
Issue number1
DOIs
Publication statusPublished - Mar 2022

ASJC Scopus subject areas

  • Software
  • Information Systems

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