Persona2vec: a flexible multi-role representations learning framework for graphs
Name
peerj-cs-439.pdf
Description
Published version
Size
2.75 MB
Format
Adobe PDF
Checksum (MD5)
4f0c073804d862b58a9561c45d249f4f
Author(s)
Yoon, Jisung
Yang, Kai-Cheng
Jung, Woo-Sung
Ahn, Yong-Yeol
Date Issued
March 30, 2021
Journal
PeerJ Computer Science
Publisher
PeerJ
Citation
Yoon J, Yang K, Jung W, Ahn Y. 2021. Persona2vec: a flexible multi-role representations learning framework for graphs. PeerJ Computer Science 7:e439
Version
Final published version
Abstract
Graph embedding techniques, which learn low-dimensional representations of a graph, are achieving state-of-the-art performance in many graph mining tasks. Most existing embedding algorithms assign a single vector to each node, implicitly assuming that a single representation is enough to capture all characteristics of the node. However, across many domains, it is common to observe pervasively overlapping community structure, where most nodes belong to multiple communities, playing different roles depending on the contexts. Here, we propose persona2vec, a graph embedding framework that efficiently learns multiple representations of nodes based on their structural contexts. Using link prediction-based evaluation, we show that our framework is significantly faster than the existing state-of-the-art model while achieving better performance.
MIT Department
MIT Connection Science (Research institute)
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.7717/peerj-cs.439
