From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs-Reference-Cited by-同舟云学术

From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs

Published:2023-11-25 Issue: Volume: Page:
ISSN:
Container-title:4th ACM International Conference on AI in Finance
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Author:

Bono Jacopo¹^ORCID,Naser Eddin Ahmad²^ORCID,Aparício David³^ORCID,Ferreira Hugo¹^ORCID,Ascensão João Tiago¹^ORCID,Ribeiro Pedro⁴^ORCID,Bizarro Pedro¹^ORCID

Affiliation:

1. feedzai, PT

2. Feedzai, PT and Departamento de Ciência de Computadores, Faculdade de Ciências, Universidade do Porto, Portugal

3. Departamento de Ciência de Computadores, Faculdade de Ciências, Universidade do Porto, PT

4. Departamento de Ciência de Computadores, Faculdade de Ciências, Universidade do Porto, Portugal

Funder

Feedzai

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3604237.3626842

Reference29 articles.

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4. Matthias Fey and Jan E. Lenssen . 2019 . Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds. Matthias Fey and Jan E. Lenssen. 2019. Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds.

5. Palash Goyal , Nitin Kamra , Xinran He , and Yan Liu . 2018 . Dyngem: Deep embedding method for dynamic graphs. arXiv preprint arXiv:1805.11273 (2018). Palash Goyal, Nitin Kamra, Xinran He, and Yan Liu. 2018. Dyngem: Deep embedding method for dynamic graphs. arXiv preprint arXiv:1805.11273 (2018).