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jgyou

@jgyou.bsky.social
1.8K followers 666 following 49 posts

Assistant professor of Statistics and Complex systems at UVM. Lab: joint-lab.github.io

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Reposted by jgyou
Network Science Society @netscisociety.bsky.social · 07/05/2026
It is our great pleasure to announce this year's Erdős-Rényi Prize winner! Laurent Hébert-Dufresne @lhd.bsky.social
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Ted Underwood @tedunderwood.com · 30/09/2025
Implementing “reasoning” as “just keep talking to yourself” feels so weird & hacky. But maybe not, according to this? They show that just-predicting-the-next-word is inherently unable to learn to solve some (permutation) problems unless you “pad” with extra thought tokens. arxiv.org/abs/2509.24125
In this technical note, we study the problem of inverse permutation learning in decoder-only transformers. Given a permutation and a string to which that permutation has been applied, the model is tasked with producing the original ("canonical") string. We argue that this task models a natural robustness property across a variety of reasoning tasks, including long-context retrieval, multiple choice QA and in-context learning. Our primary contribution is an impossibility result: we show that an arbitrary depth, decoder-only transformer cannot learn this task. This result concerns the expressive capacity of decoder-only transformer models and is agnostic to training dynamics or sample complexity. We give a pair of alternative constructions under which inverse permutation learning is feasible. The first of these highlights the fundamental role of the causal attention mask, and reveals a gap between the expressivity of encoder-decoder transformers and the more popular decoder-only architecture. The latter result is more surprising: we show that simply padding the input with "scratch tokens" yields a construction under which inverse permutation learning is possible. We conjecture that this may suggest an alternative mechanism by which chain-of-thought prompting or, more generally, intermediate "thinking" tokens can enable reasoning in large language models, even when these tokens encode no meaningful semantic information (e.g., the results of intermediate computations).
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arxiv cs.SI @arxiv-cs-si.bsky.social · 29/09/2025
Message passing for epidemiological interventions on networks with loops arxiv.org/abs/2509.21596
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jgyou @jgyou.bsky.social · 25/09/2025
Had missed this paper! It's great arxiv.org/abs/2502.01177
arxiv.org
Insights from Network Science can advance Deep Graph Learning
Deep graph learning and network science both analyze graphs but approach similar problems from different perspectives. Whereas network science focuses on models and measures that reveal the organizati...
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Ben Kawam @benkawam.bsky.social · 04/09/2025
Are you studying animal sociality? Confused about the "non-independence" of social network data: what on earth are they? are they threatening your results? should you attempt to get rid of them? A thread summarising a part of our new paper (ecoevorxiv.org/repository/v...).
ecoevorxiv.org
Five misunderstandings in animal social network analysis
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João Franco @okfranco.bsky.social · 01/09/2025
🎓 I've joined the PhD in Complex Systems & Data Science at @vcsi.bsky.social to work w/ @lhd.bsky.social & @juniperlov.bsky.social to explore social (complex) dynamics 🔬 Huge thanks to @joanagsa.bsky.social & Lília Perfeito for helping me get ready for this chapter 🚀 Excited for this next step!
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Daniel Redhead @danielredhead.bsky.social · 04/08/2025
🐒🕸️ New preprint! Confused about how to model animal social networks? ASNA can be confusing—but also full of opportunity. We break down 5 common misunderstandings in animal social network analysis and share solutions from behavioural ecology, anthro, stats, & network science. Hope it helps! A 🧵
ecoevorxiv.org
Five misunderstandings in animal social network analysis
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Daniel Redhead @danielredhead.bsky.social · 04/08/2025
All from an amazing interdisciplinary group 👥: @benkawam.bsky.social 🌟, @jgyou.bsky.social, Dan Franks, Connor Philson, Marijtje van Duijn, @jordanhart96.bsky.social, MB McElreath, @rmcelreath.bsky.social, @eapower.bsky.social, Cody Ross, @steglich.bsky.social, & @ljnbrent.bsky.social!
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Hiroki Sayama @hirokisayama.bsky.social · 04/06/2025
Leto Peel officially opens #NetSci2025, followed by Sonia Kefi's keynote
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jgyou @jgyou.bsky.social · 20/05/2025
And the code is live here: github.com/DynamicaLab/... With @allard.bsky.social and the never-online Simon Lizotte (but you can find him at siliz4.github.io)
github.com
GitHub - DynamicaLab/bigue: Bayesian inference of the hyperbolic plane embedding of complex networks.
Bayesian inference of the hyperbolic plane embedding of complex networks. - DynamicaLab/bigue
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Science of Health at Columbia University @cusciofhealth.bsky.social · 01/05/2025
Hear me discuss our new paper written with the inimitable Laurent Hébert-Dufresne and colleagues! @lhd.bsky.social #Complexity #Physiology #Governance www.nature.com/articles/s44...
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Laurent Hébert-Dufresne @lhd.bsky.social · 24/04/2025
On this day in 2023, the Vermont Complex Systems Institute (vermontcomplexsystems.org), TGIR Center (www.med.uvm.edu/tgircobre/home) & @sfiscience.bsky.social joined for an event on interacting contagions Our work, "One pathogen does not an epidemic make," is now online: arxiv.org/abs/2504.15053
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Sen Pei @senpei.bsky.social · 17/04/2025
So glad to have @jgyou.bsky.social give an intriguing lecture on contagion modeling and network inference in our network science class!
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Alec Kirkley @captainkirk1041.bsky.social · 05/04/2025
I've been guest editing a focus issue for Journal of Physics: Complexity with @jgyou.bsky.social, @gcant.bsky.social, and Cate De Bacco on Statistical Inference and Machine Learning for Complex Networks. See iopscience.iop.org/collections/..., and feel free to get in touch with any questions!
iopscience.iop.org
Focus Issue on Statistical Inference and Machine Learning for Complex Networks - IOPscienceSearch
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The Big Z @iamcurious.bsky.social · 13/03/2025
You should find that haircut again.
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Daryl Fairweather ⛅ @fairweatherphd.bsky.social · 11/03/2025
Hate The Game, the book, is only one month away! Pre-order yours anywhere books are sold. 📖✨ Freakonomics coauthor Steven Levitt called it “Fabulous—unlike any economics book I’ve ever read!
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jgyou @jgyou.bsky.social · 11/03/2025
We're excited to share our MAR10 day discounts! - $1 off of Mario's Time Machine - A free wallpaper
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Francisco Rodriguez-Sanchez @frodsan.bsky.social · 19/02/2025
1/ New paper @pnas.org on the structure of mutualistic #networks between individuals plants and frugivore species. Last chapter of @elequintero.bsky.social's PhD thesis doi.org/10.1073/pnas... #ecopubs
Downscaling mutualistic networks from species to individuals reveals consistent interaction niches and roles within plant populations

Species-level networks emerge as the combination of interactions spanning multiple individuals, and their study has received considerable attention over the past 30 y. However, less is known about the structure of interaction configurations within species, even though individuals are the actual interacting units in nature. We compiled 46 empirical, individual-based, interaction networks on plant-animal seed dispersal mutualisms, comprising 1,037 plant individuals across 29 species from various regions. We compared the structure of individual-based networks to that of species-based networks and, by extending the niche concept to interaction assemblages, we explored individual plant specialization. Using a Bayesian framework to account for uncertainty derived from sampling, we examined how plant individuals “explore” the interaction niche of their populations. Both individual-based and species-based networks exhibited high variability in network properties, lacking remarkable structural and topological differences between them. Within populations, frugivores’ interaction allocation among plant individuals was highly heterogeneous, with one to three frugivore species dominating interactions. Regardless of species or bioregion, plant individuals displayed a variety of interaction profiles across populations, with a consistently-small percentage of individuals playing a central role and exhibiting high diversity in their interaction assemblage. Plant populations showed variable mid to low levels of niche specialization; and individuals’ interaction niche “breadth” accounted for 70% of the population interaction diversity, on average. Our results highlight how downscaling from species to individual-based networks helps understanding the structuring of interactions within ecological communities.
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Alessandro Vespignani @alexvespi.bsky.social · 12/02/2025
Wastewater from airplane toilets? We introduce a global Aircraft-Based Wastewater Surveillance Network (WWSN) for pandemic monitoring in Nature Medicine 🔗 doi.org/10.1038/s415... Aircraft-based wastewater surveillance allows for real-time, non-invasive monitoring of global pathogen spread Short 🧵
doi.org
Pandemic monitoring with global aircraft-based wastewater surveillance networks - Nature Medicine
By simulating the implementation of airport-based wastewater surveillance sites at the global level, a modeling study shows how this early warning system would perform in identifying sources of pandem...
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Will Thompson @willthompson.bsky.social · 23/11/2024
On a similar note I got spoiled by Dr Watson in Julia. Is there a good Python equivalent?
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Andrew Mercer @awmercer.bsky.social · 14/01/2025
We are reaching the time of year when I start hearing from undergrads who are looking for summer internships. Obviously, they should all apply to Pew, but what other research orgs have internship programs that I can tell them about? What other opportunities can I share with them?
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jgyou @jgyou.bsky.social · 14/01/2025
24h left to apply to the REU experience at UVM! Harnessing the Data Revolution for Vermont: The Science of Online Corpora, Knowledge, and Stories (SOCKS) socks.w3.uvm.edu/socks/node/65
socks.w3.uvm.edu
SOCKS Undergraduate Internships | SOCKS
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Michael "Shapes Dude" Betancourt @betanalpha.bsky.social · 20/12/2024
Comparison may be a thief of joy, but it is an undoubtably interesting statistical modeling problem. In my latent chapter I go deep into the general problem of modeling comparisons between pairs of abstract items. HTML: betanalpha.github.io/assets/chapt... PDF: betanalpha.github.io/assets/chapt...
betanalpha.github.io
Pairwise Comparison Modeling
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Tim Waring @twaring.bsky.social · 17/12/2024
Fully-funded PhD position on the cultural evolution of environmental management! Link evolution, ecology and behavioral science to study how people manage environmental challenges in the ACE Lab (timwaring.info). Position Details: timwaring.info/wp-content/u...
timwaring.info
ACE Lab: Applied Cultural Evolution Laboratory
culture, evolution, cooperation, and sustainability
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jgyou @jgyou.bsky.social · 16/12/2024
I preemptively declare paper bankruptcy. Anything published on arXiv during the next 3 weeks will be permanently memory black holed.
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Laurent Hébert-Dufresne @lhd.bsky.social · 10/12/2024
A project with friends from the Vermont Complex Systems Institute @juniperlov.bsky.social @jstonge.bsky.social @nwlandry.bsky.social @jgyou.bsky.social Funded by an ambitious AUDACE project from friends and co-authors from Université de Sherbrooke. Started in the Green Mountains two years ago.
Fall retreat in Vermont where the project was started. Panorama with a field and hills in the distance.
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Sam Scarpino @scarpino.bsky.social · 26/11/2024
1/ Influenza wastewater activity levels are currently low across the US, aside from conspicuous areas in CA where #H5N1 is spreading in dairy cows. A deeper dive into the Wastewater Scan data paints a very concerning picture and should add to our sense of urgency.
Map shows the location of wastewater sampling locations around the US. Most of the sites indicate low levels and are colored blue. However, there are a number of sites in CA with high levels and few other scattered sites around the US with medium/high levels and are colored in orange/red
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Ricky @ricky.fm · 21/11/2024
what idiot called it async/await instead of wait/what
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Alex Thiery @alexxthiery.bsky.social · 20/11/2024
Gaussian approximation of a target distribution: mean-field versus full-covariance! Below shows a simple gradient descent on KL(q||p)
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Jonathan Edward Durham @thisone0verhere.bsky.social · 20/11/2024
Having a sides-only Thanksgiving this year. No turkey, just carbs and gravy. Modern problems require modern solutions.
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jgyou @jgyou.bsky.social · 20/11/2024
👋
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jgyou @jgyou.bsky.social · 20/11/2024
Wasn't the Kalshi case well on its way *before* the end of Chevron, though?
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jgyou @jgyou.bsky.social · 19/11/2024
Ah! Completely misunderstood. Interesting. I would love to see it. Seems pretty easy too. Don't know of any.
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jgyou @jgyou.bsky.social · 19/11/2024
The DOGE should get rid of the NSF COA template and then fold
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jgyou @jgyou.bsky.social · 19/11/2024
wdym? Whether the controlling organization has a content creator program like Perplexity's? Or a benchmark of the quality of source attribution at generation?
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jgyou @jgyou.bsky.social · 19/11/2024
"yeah but you can calculate probabilities as pnorm(1) instead of like pnorm(20, 10, 10)"
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jgyou @jgyou.bsky.social · 19/11/2024
I'm teaching Applied Probability and my cold take is that the standard normal is basically useless for practitioners in an age of R.
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jgyou @jgyou.bsky.social · 18/11/2024
corpus.ulaval.ca/server/api/c... gives the equilibrium solution + AME evolution equation in finite size. Gaps: * the process doesn't account for directions on edges. * Like @tiago.skewed.de pointed out, I think sub-sampling effects are probably needed to fully explain the structure too.
corpus.ulaval.ca
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jgyou @jgyou.bsky.social · 18/11/2024
Structural preferential attachment has two parameters that can control the scaling exponents + pre-tail behavior
Analytical predictions (lines) and simulations (markers) for (a) an approximation of link-based system
using a node-based SPA process and (b) the link-based SPA using the same parameters as in (a). (c) Community structure of the cond-mat
arXiv as measured by a link community algorithm [5] (dots) and as modeled by a link-based SPA (q2 = 0.95; p2 = 0.39) in continuous lines
or by a node-based SPA which approximates the link-based system [q1 = 0.68 and p1 = 0.56 according to Eq. (24)] in dashed lines. The two black lines perfectly overlap, while the node-based membership distribution is slightly shifted by the use of approximation (22).
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jgyou @jgyou.bsky.social · 18/11/2024
The inference should be pretty straightforward with temporal data. All of these models are simple Markov processes. It is a much more difficult problem if using only structural data---though I guess one can run simple statistical tests using the degree distributions as test statistics.
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Michael "Shapes Dude" Betancourt @betanalpha.bsky.social · 17/11/2024
Greetings recent followers. ┏┓ ┃┃╱╲ in ┃╱╱╲╲ this ╱╱╭╮╲╲ house ▔▏┗┛▕▔ we ╱▔▔▔▔▔▔▔▔▔▔╲ don't just talk about data generating processes; we actually model them and then critique our assumptions. ╱╱┏┳┓╭╮┏┳┓ ╲╲ ▔▏┗┻┛┃┃┗┻┛▕▔
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jgyou @jgyou.bsky.social · 17/11/2024
@lhd.bsky.social wrote a model back in 2011 that I think applies here journals.aps.org/prl/abstract.... Growth is currently driven by cliques (starter packs) and this leads to different scaling relationships.
journals.aps.org
Structural Preferential Attachment: Network Organization beyond the Link
We introduce a mechanism which models the emergence of the universal properties of complex networks, such as scale independence, modularity and self-similarity, and unifies them under a scale-free org...
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jgyou @jgyou.bsky.social · 17/11/2024
Pictured: Sea of Stars by @sabotagestudio.com, Tape to Tape by @excellentrectangle.bsky.social and Ship of Fools by Fika Production.
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jgyou @jgyou.bsky.social · 17/11/2024
Québec indie game studios are killing it lately. Just in the past year or so, I've played three amazing games that *happened* to be from my hometown. Way to go!
Screenshot of Sea of Stars, an indie RPGScreenshot of Tape to Tape, an indie hockey-themed roguelikeScreenshot of Sea of Fools, an indie coop rogue like
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‏ deepfates @deepfates.com.deepfates.com.deepfates.com.deepfates.com.deepfates.com · 15/11/2024
Reminder that if you put stuff on Bluesky can be accessed by basically anybody in the world, and it's totally legal for other people to train on your data Even if the Bluesky team says they won't. Also AI is good actually
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jgyou @jgyou.bsky.social · 15/11/2024
Submission by May 2025. Co-edited with Alec Kirkley, Cate De Bacco and @gcant.bsky.social
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jgyou @jgyou.bsky.social · 15/11/2024
I am reposting this here! I'm co-editing a focus Issue on "Statistical Inference and Machine Learning for Complex Networks" At the Journal of Physics Complexity. iopscience.iop.org/collections/... Feel free to get in touch if you have questions: jyoung22@uvm.edu.
iopscience.iop.org
IOPscience - Focus Issue on Statistical Inference and Machine Learning for Complex NetworksSearch
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Why @why.bsky.world · 14/11/2024
Features users want: - group DMs - custom notification filters - something about gifs Features we are prepared to deliver: - servers not going down under the load of most of twitter joining - some cute animations when you expand a picture in your feed
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jgyou @jgyou.bsky.social · 14/11/2024
Born too late for LinkedIn. Too early to explore the stars. But just in time for bsky.
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NetSci 2026 @netsciconf.bsky.social · 12/11/2024
Want to contribute and help lead our community? NetSci 2025 is soliciting applications to its program committee! We especially encourage early career researchers to apply. forms.gle/98rhcJeoVVZG...
forms.gle
NetSci2025 Programme Committee form
Thank you for your interest in joining the NetSci 2025 Programme Committee. We are happy to welcome new members in our programme committee from a variety of fields and from different career stages. ...
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