paninid, to internet
@paninid@mastodon.world avatar

Credit to @blogdiva for a approach to connect the dots of psyops-as-a-service with the social network information ecosystem treated as Patient Zero with APIs the contagion vector.

brooklynsoc, to Sociology

In my data analysis and visualization course, we're ending the semester discussing network analysis. We spent last week talking about where to find network data and how to build a network dataset to use with networkx in python. This week, we're looking deeper at plotting and analytical questions about networks. This is an example using the New Zealand Legislation Network data (source: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/TZOJBU).

adulau, to opensource

If you wrote scripts using tshark, there are some changes in version 4.2.0 of wireshark for the -e option...

🔗 https://www.wireshark.org/docs/relnotes/wireshark-4.2.0.html

misp, to opensource

misp-wireshark v1.1 released including support for tshark, installation error and various improvements.

misp-wireshark is a Lua plugin intended to help analysts extract data from Wireshark and convert it into the MISP Core format

https://github.com/MISP/misp-wireshark

a, to python
@a@paperbay.org avatar

A new release 3.2 of the incredible library NetworkX (the Network Analysis tool in Python)

Many improvements including Kemeny's constant on undirected and directed graphs.

🔗 Release notes - https://networkx.org/documentation/stable/release/release_3.2.html

just_mobility, to random German

Betweenness Centrality in Spatial Networks: Our latest publication is available online at https://doi.org/10.4230/LIPIcs.GIScience.2023.83.

We analysed how #spatial density of nodes influences betweenness #centrality and provide a generic method for mitigating these effects. #SpatialCentrality

Looking forward to presenting and discussing our findings next Wednesday at #GIScience2023 conference.
#reproducibility: find data and code at https://doi.org/10.5281/zenodo.8125632

#NetworkAnalysis #UrbanAnalytics #GIScience

openfuture, to random
@openfuture@eupolicy.social avatar

What exactly is the ? And what are its boundaries? Looking for answers, together with a new media art & design collective, panGenerator, we conducted exploratory mapping using a method fuelled with Twitter data: https://openfuture.eu/publication/fields-of-open/ 🧶 1/3

da5nsy, to mastodon
@da5nsy@social.coop avatar

Are there any good tools for analysing your Mastodon network in a / kind of way?

I'd love to:

  • visualise the different groups of people that I follow: what are the connections between the people that I follow?
  • see which accounts post a lot, or not at all
  • see which accounts I interact with the most (are there people that I followed but it turns out I'm not actually interested in what they post now?)
  • see what hours I and others post

crideaukikuchi, to random French


Pour une formation, je fais mumuse avec Gephi. Eh bien il faut avouer que ce machin est bien plus intuitif que Visone que j'utilisais jusque là.
Vive l'analyse de réseaux !

  • All
  • Subscribed
  • Moderated
  • Favorites
  • JUstTest
  • ngwrru68w68
  • everett
  • InstantRegret
  • magazineikmin
  • thenastyranch
  • rosin
  • GTA5RPClips
  • Durango
  • Youngstown
  • slotface
  • khanakhh
  • kavyap
  • DreamBathrooms
  • provamag3
  • tacticalgear
  • osvaldo12
  • tester
  • cubers
  • cisconetworking
  • mdbf
  • ethstaker
  • modclub
  • Leos
  • anitta
  • normalnudes
  • megavids
  • lostlight
  • All magazines