A Rényi Intézet Deep Learning szemináriumának következő előadását Szakács Lili Kata (ELTE) és Béres Ferenc (SZTAKI) tartja május 18-án 16:00-kor, melyhez a Zoom link ide kattintva érhető el. Az előadás nyelve magyar.

Absztrakt:

Graph embedding methods and applications
Szakács Lili Kata (ELTE), Béres Ferenc (SZTAKI)

In this talk, we give a quick introduction to embedding nodes and whole
graphs in vector spaces [1]. We highlight the connection to word
representation learning and discuss how graph embedding models process
the raw graph to gain meaningful representations. We also address two of
our applications. In [2], we collected Ethereum-related data from
multiple sources (Twitter, Etherscan, Tornado cash) to deanonymize
Ethereum users. In [3], we collected Twitter data related to Covid-19
and classified tweets based on the expressed vaccine view.

[1] Rozemberczki, B., Kiss, O., & Sarkar, R. (2020). Karate Club: An API
Oriented Open-Source Python Framework for Unsupervised Learning on
Graphs. Proceedings of the 29th ACM International Conference on
Information & Knowledge Management, 3125–3132.
https://doi.org/10.1145/3340531.3412757

[2] Béres, F., Seres, I. A., Benczúr, A. A., & Quintyne-Collins, M.
(2021). Blockchain is Watching You: Profiling and Deanonymizing Ethereum
Users. 2021 IEEE International Conference on Decentralized Applications
and Infrastructures (DAPPS), 69–78.
https://doi.org/10.1109/DAPPS52256.2021.00013

[3] Béres, F., Csoma, R., Michaletzky, T. V., & Benczúr, A. A. (2021).
Vaccine skepticism detection by network embedding. Book of Abstracts of
the 10th International Conference on Complex Networks and Their
Applications, 241–243.