Joined September 2020
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šŸ“–New paper featured in @NatureComms ! Many THX again to @MConstanceCorsi and @F_DeVicoFallani for their trust and commitment in the project. Would you take a hint of darkside in your multilayer networks ? šŸ‘‡ nature.com/articles/s41467-0…

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1 month ago, I defended my #phd entitled "Characterization of multilayer networks: theory and applications to the brain", under the memorable supervision of @F_DeVicoFallani. Thanks to @_AlexArenas @LindaDouw, @alainbarrat, @gin_bianconi for our insightful discussions.
Few weeks ago @CPresigny @inria_paris @InstitutCerveau successfully defended his #PhD where we explored the dark side of complex networks arxiv.org/abs/2306.12136 Insightful discussion w\ committee members @_AlexArenas @gin_bianconi @LindaDouw @alainbarrat
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I now hold a #phd from @Sorbonne_Univ_ ! Thanks to all my colleagues at the @AramisLabParis for this wonderful PhD journey. Link to the thesis manuscript: theses.hal.science/tel-04261… Stay in touch for the next steps of my academic journey šŸ“–!

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Let us explore together the dark side of multilayer networks at 3.45 pm (HS 1 - Network structure) under the sun of Vienna - contributed talk !ā˜€ļø @netsci2023 @F_DeVicoFallani #talks #multilayer
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ArXiv submission !šŸ“– Get ready to explore the dark side of multilayer networks ! Thx so much to @MConstanceCorsi , @F_DeVicoFallani for their invaluable contributions at different stages of this work ! 🧵 arxiv.org/abs/2306.12136

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We exploit the complementarity of the two descriptions to provide a local connectivity-based characterization of very diverse multiplex networks. Actually, we need both descriptions to properly classify groups of multiplex networks !
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Which description best discriminate MEG activity of Alzheimer's disease patients as compared with the healthy controls one ? 🧠 The dark side seems promising in this way. How about we explore it further ?🧐
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One week ago, the in-person @APSMeetings 2023 ended. Warm thx to all of the organizers and volunteers that made it possible. Special thx to @GuidoCaldarelli for chairing the session where I presented. Lots of new ideas to explore, now ! #APSMarch2023 #Physics #Science #Networks
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Make sure to be present in Room 125 @APSMeetings for a Focus session on Network Theory and Applications. My talk on node-layer duality in multilayer networks is expected at 9:36 a.m PT. See you there ! #apsmarch #physics #network
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DAY 24 - "The structural neural substrate of subjective happiness", Wataru Sato et al., Sci. Rep. (2015) doi.org/10.1038/srep16891 Last candy from the advent article event. Where is located the subjective happiness? Thx for following this 1 month journey ! Merry Xmas !šŸŽ„ Cheers !
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DAY 22 - "Moving to a World beyond "p<0.05"", Ronald L. Wasserstein, Allen L. Schism and Nicole A. Lazar, The American Statistician (2019) doi.org/10.1080/00031305.201… Editorial introducing a 43 papers issue to constructively replace/reform the use of p-values in science. 🧵

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- Reforming p-values cannot be done without revolutionizing research, publishing, funding and training practices: train editors and funding committees on new statistical practices, generalized "result-blind" pre-registered method ...
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, train graduate student to replicate studies, update online publications to mention if results were replicated. - Anyway moving beyond p values is simply about a paradigm shift in science activities (way beyond p values) - Will it (bayesian) update your science practices ? /end
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DAY 21 - "Statistical inference links data and theory in network science", Leto Peel, Tiago P. Peixoto and Manlio De Domenico, Nat. Commun. (2022) doi.org/10.1038/s41467-022-3… Bayes law must bring network science to the next level: a milestone perspective. 🧵 #network #inference
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- Unlike many quantitative domains, uncertainties are almost never assessed, which can be highly detrimental to further analysis (Fig 1 in the paper) - Hypothesis on the representation of networks should be explicit (with a generative modelšŸ˜‡), ex in correlation networks...
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- No blind applications of network metrics especially in deriving null models and again, assess uncertainties on them - (Aside) Validate method on synthetic data. - Bayesian inference can endorsed the role of a principled based "common language" of network scientists. /end
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