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: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Amsterdam
Elsevier B.V
01.01.2020
Elsevier Science Ltd |
| Témata: | |
| ISSN: | 0950-7051, 1872-7409 |
| On-line přístup: | Získat plný text |
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| Abstract | 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. |
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| AbstractList | 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. |
| ArticleNumber | 104816 |
| Author | Chhetri, Sujit Rokka Canedo, Arquimedes Goyal, Palash |
| Author_xml | – sequence: 1 givenname: Palash surname: Goyal fullname: Goyal, Palash email: palashgo@usc.edu organization: University of Southern California, Information Sciences Institute, 4676 Admiralty Way, Suite 1001, Marina del Rey, CA 90292, USA – sequence: 2 givenname: Sujit Rokka surname: Chhetri fullname: Chhetri, Sujit Rokka organization: University of California-Irvine Irvine, CA 92697, USA – sequence: 3 givenname: Arquimedes surname: Canedo fullname: Canedo, Arquimedes organization: Siemens Corporate Technology, 755 College Rd E, Princeton, NJ 08540, USA |
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