Check it out!
Paper: lnkd.in/dkNs8_F2
Code's documentation: lnkd.in/dWkiGfsX
Paper on temporal graph distance: www.nature.com/articles/s41...

Building on our result, we design a task-agnostic embedding algorithm in the 2Vec spirit which can be easily adapted to arbitrary embedding problems. This algorithm is also performing the heavy lifting for our definition of distance between temporal graphs.

In this paper, we propose an efficient linear-time approximation of SoftMax normalization constants, a well-known and hard problem in machine learning.

New publication!

I am happy to announce that the paper "Learning distributed representations with efficient SoftMax normalization" I authored with Enrico Maria Belliardo has been published in Transactions of Machine Learning Research @tmlrorg.bsky.social.

🚨Happy to share our latest study on the resilience of the mobility network to first and second wave Covid-19 interventions in Chile. 🦠
Thanks to Pasquale Casaburi for leading this and the team!
🔗 journals.plos.org/ploscompbiol...

New paper out in Nature Communications: www.nature.com/articles/s41...
With Lorenzo Dall'Amico and @alainbarrat.bsky.social, we define a distance between temporal networks and explore its sensitivity to complex features in empirical temporal network data. @isifoundation.bsky.social

Kicking off the EcoSocEpi workshop at @isifoundation.bsky.social. #epidemics