@lenamangold
Complex networks and computational social science. Postdoc at @invcomplexity.skewed.de (IT:U Austria) - formerly at @cams.ehess.fr (CNRS & EHESS). Once data scientist at UK Labour & Datasketch (Bogota). https://lenafm.github.io/
New blog post:
"Higher orders need higher standards"
skewed.de/lab/posts/hi...
I discuss our current work disentangling misconceptions around "higher-order" networks: arxiv.org/abs/2602.16937
Explainer thread for the paper here: bsky.app/profile/tiag...
🎓 Short interview with me by the lovely people at @cams.ehess.fr , where I wrapped up my PhD this summer ✨ In 🇫🇷, with a longer version also available in 🇬🇧: shorturl.at/FjNRA
New preprint: “Multiscale patterns of migration flows in Austria: regionalization, administrative barriers, and urban-rural divides”, with @thomrobiglio.bsky.social, Martina Contisciani, @martonkarsai.bsky.social, arxiv.org/abs/2507.11503
Last week we had our second “Inverse Complexity Lab Retreat” at wonderful Traunkirchen!
skewed.de/lab/group.ht...
Nothing like eldritch C++ incantations by the sunny lakeside! 🏖️
Come join us tomorrow afternoon (2nd June) at WiNS @ NetSci: the future of Network Science (Location: FPN Tongeren zaal)!
Keynotes by @asteixeira.bsky.social and @katyborner.bsky.social, followed by lightining talks / posters from the WiNS community :)
🚨 Acceptance Decisions Are In! 🚨
We’ve finalized the acceptance decisions for the submissions received before the priority deadline. Make sure to check your inbox!
✨ We’re still accepting submissions on a rolling basis! Submit your abstract here: tinyurl.com/winsNetSci25
📅 Don't miss out on our biweekly seminar series!
Next Monday (Feb 24th, 11 AM ET), we'll have
@gulsahakcakir.bsky.social
from the University of California, Los Angeles. She’ll be presenting her work titled "Copy or Collaborate? Optimal Networks for Collective Problem Solving."
See you all there!✨
If you're an early-career network scientist and want to present your work (in progress) to a supportive and interdisciplinary audience: Priority deadline for the WiNS satellite at NetSci25 is today! 🚨
WiNS is on Bluesky! Follow for updates on events, opportunities and new research ✨
Cool new paper out by my colleagues on filter bubbles on music streaming platforms - go check it out :)
flyer of the satellite organized by Women in Network Science
Women in Network Science is hosting a half-day satellite at @netsciconf.bsky.social 2025 in Maastricht 🇳🇱! 🌟
Submit your abstract here:
forms.gle/nEY4qWTnMuwL...
Deadline: February 17th
Full event details:
sites.google.com/view/womenin...
#NetSci2025 #WomenInNetworkScience
Check out the code on our repository! (5/5)
github.com/lenafm/metab...
We showcase how metablox works on a number of synthetic and empirical networks, and that it can be used in comparative settings: if you have an entire collection of networks with shared metadata OR a network with multiple sets of categorical metadata. (4/5)
In our paper, we introduce the metablox tool, to quantify the strength and type of relationship between categorical node metadata and the block structure of a network (using Stochastic block models and description length). (3/5)
Different sets of node metadata may (a) relate to a network’s block structure to varying degrees, and (b) resemble different types of arrangements, such as community structure or core-periphery structure! (2/5)
New paper out (with @camcom.bsky.social) 🥳
Want to understand how your network's metadata relate to its block structure? Check out our 'metablox' tool! @CommsPhys
"Quantifying metadata relevance to network block structure using description length" 🧵(1/5)
www.nature.com/articles/s42...
New paper!
“Scalable network reconstruction in subquadratic time”
arxiv.org/abs/2401.01404
TL;DR: It's now possible to reconstruct huge networks from observational data using statistical inference.
Explainer thread: 1/N
Our model serves as a benchmark for future research, to gauge how mesoscale structure detection algorithms deal with mesoscale ambiguity + we emphasise the importance of considering coexisting structures. (6/6)
In our experimental set-up, the coexistence of the two partitions was only detected in a small number of cases — mostly one (dominating) structure was preferred! (5/6)
We showcase the SCBM by generating networks with coexisting bi-community and core-periphery structure (with varying ‘structural strength’) and we explore how well they are picked up by different SBM variants. (4/6)
We introduce the Stochastic Cross-Block Model (SCBM), a generative framework for networks with multiple coexisting ground-truth partitions. (3/6)
Real networks often feature multiple coexisting structures which community detection algorithms may or may not pick up, calling for a benchmark network model with built-in ambiguity on the mesoscale. (2/6)
First ever published paper (with @camcom.bsky.social ) seems like a good reason for a first post here :)
"Generative models for two-ground-truth partitions in networks" 🧵 (1/6)
journals.aps.org/pre/abstract...
Our model serves as a benchmark for future research, to gauge how mesoscale structure detection algorithms deal with mesoscale ambiguity + we emphasise the importance of considering coexisting structures. (6/6)
In our experimental set-up, the coexistence of the two partitions was only detected in a small number of cases — mostly one (dominating) structure was preferred! (5/6)
We showcase the SCBM by generating networks with coexisting bi-community and core-periphery structure (with varying ‘structural strength’) and we explore how well they are picked up by different SBM variants. (4/6)
We introduce the Stochastic Cross-Block Model (SCBM), a generative framework for networks with multiple coexisting ground-truth partitions. (3/6)
Real networks often feature multiple coexisting structures which community detection algorithms may or may not pick up, calling for a benchmark network model with built-in ambiguity on the mesoscale. (2/6)