تقرير
Persona2vec: A Flexible Multi-role Representations Learning Framework for Graphs
| العنوان: | Persona2vec: A Flexible Multi-role Representations Learning Framework for Graphs |
|---|---|
| المؤلفون: | Yoon, Jisung, Yang, Kai-Cheng, Jung, Woo-Sung, Ahn, Yong-Yeol |
| سنة النشر: | 2020 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Computer Science - Social and Information Networks, Computer Science - Machine Learning, Statistics - Machine Learning |
| الوصف: | 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. Comment: 9 pages, 7 figures |
| نوع الوثيقة: | Working Paper |
| الوصول الحر: | http://arxiv.org/abs/2006.04941Test |
| رقم الانضمام: | edsarx.2006.04941 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |