dyngraph2vec: Capturing network dynamics using dynamic graph representation learning

Learning graph representations is a fundamental task aimed at capturing various properties of graphs in vector space. The most recent methods learn such representations for static networks. However, real-world networks evolve over time and have varying dynamics. Capturing such evolution is key to pr...

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Vydáno v:Knowledge-based systems Ročník 187; s. 104816
Hlavní autoři: Goyal, Palash, Chhetri, Sujit Rokka, Canedo, Arquimedes
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 01.01.2020
Elsevier Science Ltd
Témata:
Datasets
Dynamics
Efficacy
Embedding
Evolution
Graph embedding applications
Graph embedding techniques
Graph representations
Graphical representations
Graphs
Learning
Machine learning
Networks
Performance prediction
Predictions
Property
Python graph embedding methods GEM library
Recurrent
Stochastic models
Python graph embedding methods GEM library
Graph embedding applications
Graph embedding techniques
ISSN:0950-7051, 1872-7409
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Shrnutí:Learning graph representations is a fundamental task aimed at capturing various properties of graphs in vector space. The most recent methods learn such representations for static networks. However, real-world networks evolve over time and have varying dynamics. Capturing such evolution is key to predicting the properties of unseen networks. To understand how the network dynamics affect the prediction performance, we propose an embedding approach which learns the structure of evolution in dynamic graphs and can predict unseen links with higher precision. Our model, dyngraph2vec, learns the temporal transitions in the network using a deep architecture composed of dense and recurrent layers. We motivate the need for capturing dynamics for the prediction on a toy dataset created using stochastic block models. We then demonstrate the efficacy of dyngraph2vec over existing state-of-the-art methods on two real-world datasets. We observe that learning dynamics can improve the quality of embedding and yield better performance in link prediction.
Bibliografie:ObjectType-Article-1
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2019.06.024