Low-Dimensional Statistical Manifold Embedding of Directed Graphs
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Author / Producer
Date
2020
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
We propose a novel node embedding of directed graphs to statistical manifolds, which is based on a global minimization of pairwise relative entropy and graph geodesics in a non-linear way. Each node is encoded with a probability density function over a measurable space. Furthermore, we analyze the connection between the geometrical properties of such embedding and their efficient learning procedure. Extensive experiments show that our proposed embedding is better preserving the global geodesic information of graphs, as well as outperforming existing embedding models on directed graphs in a variety of evaluation metrics, in an unsupervised setting.
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Publication status
published
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Editor
Book title
8th International Conference on Learning Representations (ICLR 2020)
Journal / series
Volume
3
Pages / Article No.
2018 - 2035
Publisher
Curran
Event
8th International Conference on Learning Representations (ICLR 2020) (virtual)
Edition / version
Methods
Software
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Date collected
Date created
Subject
Graphs; Network Embedding; LEARNING ALGORITHMS (MATHEMATICAL STATISTICS); Machine learning (artificial intelligence)
Organisational unit
03784 - Helbing, Dirk / Helbing, Dirk
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Related publications and datasets
Is new version of: https://arxiv.org/abs/1905.10227