Can I read No PANE, No Gain: Scaling Attributed Network Embedding in a Single Server on EtoBox?

No PANE, No Gain: Scaling Attributed Network Embedding in a Single Server by Renchi Yang; Jieming Shi; Xiaokui Xiao; Yin Yang; Sourav S. Bhowmick; Juncheng Liu is a scholarly article available to read on EtoBox.

What is No PANE, No Gain: Scaling Attributed Network Embedding in a Single Server about?

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.

Author

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

Publisher

ACM

Published

2022

Language

EN