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Persona2vec: a flexible multi-role representations learning framework for graphs

Yoon, Jisung / Yang, Kai-Cheng / Jung, Woo-Sung / Ahn, Yong-Yeol

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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 proposepersona2vec, 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.

Issue Date
2021-03
Publisher
PeerJ Inc.
Keywords(Author)
Graph Embedding; Overlapping Community; Social Context; Social Network Analysis; Link Prediction
DOI
10.7717/peerj-cs.439
Journal Title
PeerJ Computer Science
Start Page
e439
ISSN
2376-5992
Language
English
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