RE: https://scicomm.xyz/@tfardet/116686789927233210
Mentioning this specifically also for the #complexSystems and #networkScience community on here, in case you know students that might be interested 😉
Same for people in fields like #computationalSociology or #computationalHumanities
📚 In my review this week, I delve into the book of Albert-László Barabási, Linked – a fascinating journey into the hidden science of networks. Check it out now on my biweekly book review blog.
Read my post here: https://booksilike.eu/albert-laszlo-barabasi-linked/
#AlbertLaszloBarabasi #BookReview #BooksILike #LinkedBook #NetworkScience
What percentage of Fediverse accounts are connected by follow relationships directly or indirectly to @Gargron?
I wonder how big the largest connected component (LCC) of the Fediverse social graph is, where each node is a person and each edge is a follow relationship.
FediIndex shows 18,400+ user accounts. My guess is maybe 10% of them are unreachable through the follow graph.
It'd be interesting to find out!
🧠 What if missing data is not a flaw, but one of the most informative parts of a complex system?
🔗 Informative Missingness in Nominal Data: A Graph-Theoretic Approach to Revealing Hidden Structure. Computational and Structural Biotechnology Journal (CSBJ). DOI: https://doi.org/10.34133/csbj.0099
📚 CSBJ - A Science Partner Journal: https://spj.science.org/journal/csbj
#DataScience #BigData #GraphTheory #ComputationalBiology #NetworkScience #Bioinformatics #SystemsBiology #BiomedicalResearch #MissingData
DataSci.social is a Mastodon server for researchers & practitioners in human-centric data science, broadly defined. For example human-centric network science, social data science, computational social science, geospatial data science.
You can find out more at https://datasci.social/about or contact the admin account @mszll
#FeaturedServer #DataScience #DataSci #NetworkScience #SocialScience #Geospatial #Mastodon #Fediverse #FreeFediverse
New paper. With Ekaterina Vasileva, Liubov Tupikina, Dmitry Fedorov, Daniil Musatov, Andrei Raigorodskii and Stefano Boccaletti.
The naive generalization of the concept of distance to hypergraphs is equivalent to applying a clique-projection approximation. However, this is known to induce loss of information, especially in networks where the higher-order interactions are very important. To fix this problem,we introduce a new definition of distance on weighted higher-order networks, which includes the case of unweighted hypergraphs and classic graph distance as particular cases, and allows one to account for different meanings associated to the weights. We also show what difference this makes in analyses of real-world data.
https://www.nature.com/articles/s42005-026-02592-w
#mathematics #physics #graphtheory #graphs #hypergraphs #higherordernetworks #networkscience #networks
The concept of distance in graphs and hypergraphs faces challenges when extended to weighted hypergraphs due to potential inconsistencies. The authors propose a well-defined distance measure for weighted hypergraphs and demonstrate its applicability on real-world datasets, showing that the use of the measure may help to avoid the information loss typically arising when standard approaches are used.
Tanguy Fardet
Discover My Europe
Evan Prodromou
Manlio De Domenico
CSBJ
Fedi.Garden 🏳️🌈 🏳️⚧️
Charo del Genio