Persona2vec: A Flexible Multi-role Representations Learning Framework for Graphs

• URL: http://arxiv.org/abs/2006.04941v2
• Date: Wed, 21 Oct 2020 14:05:31 GMT
• Title: Persona2vec: A Flexible Multi-role Representations Learning Framework for Graphs
• Authors: Jisung Yoon, Kai-Cheng Yang, Woo-Sung Jung, and Yong-Yeol Ahn
• Abstract summary: persona2vec is a graph embedding framework that efficiently learns multiple representations of nodes based on their structural contexts. We show that our framework is significantly faster than the existing state-of-the-art model while achieving better performance.
• Score: 4.133483293243257
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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