#igraph — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #igraph, aggregated by home.social.
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🤩 Fantastic new network plotting package available in Python by Fabio Zanini. The package supports both #networkx :networkx: and #igraph :igraph: networks, and has a wide variety of styling options.
https://iplotx.readthedocs.io/en/latest/Reposting on Mastodon - Source: https://bsky.app/profile/vtraag.bsky.social/post/3m2bjcckons2f
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🧩 [TIP] – {igraph} —
Modelá grafos, calculá centralidades y detectá comunidades. Ideal para análisis relacional y visualizaciones con ggraph.
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WOW!
The ig_degree_betweenness python module has hit 747 downloads just 2 days after its release!
If you work with social network analysis and want to detect clusters with two major popularity metrics, check out the ig_degree_betweenness - available in Python and R!
GitHub repositories in the comments below!
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Calculating different #centrality measures for a street #network takes longer than expected.
#DegreeCentrality is calculated in a few milliseconds. But oh boy. #ClosenessCentrality and #BetweennessCentrality are proper whoppers. For a network of 65 000 nodes, we're talking about 2+ hour calculation times for the closeness centrality, not to mention the betweenness.
Apparently switching to #igraph would provide a speed boost over #networkx but the convenience of #osmnx has won me over.
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The "Smith-Pittman" algorithm is now available as a Python implementation for #igraph users!
Leverage node degree and edge betweenness in community detection with this Girvan-Newman styled algorithm.
Remember to star the repo here: https://github.com/benyamindsmith/ig_degree_betweenness_py/
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Calling all #igraph enthusiasts!
We've identified and fixed a bug in {ig.degree.betweenness} related to the cluster_edge_betweenness() function.
The issue stemmed from a grep() action used for subgraph identification.
A fix has been implemented, and an update has been pushed to CRAN—it will be available in the coming days.
In the meantime, you can reinstall from the main branch here: https://github.com/benyamindsmith/ig.degree.betweenness
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Calling all R and igraph enthusiasts!
FYI: there has been a bug noticed in the cluster_edge_betweenness code with the grep() action involved with selecting subgraphs for nodes. A new update has been pushed to CRAN and will be released in the coming days.
Reinstall from the main branch here for now: https://github.com/benyamindsmith/ig.degree.betweenness
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If you want to geek more about {igraph}. Check out the (UnOfffical) discord server with core members in it!
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🙏 Big thanks to the #rstats #geospatial and #igraph communities for giving {ig.degree.betweenness} so much love by checking it out!
I didn't think it would get this much attention! I am grateful to every single one of you for giving it a spin!
Give it a rip if you havent already: https://github.com/benyamindsmith/ig.degree.betweenness/
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Please help - #networkScience question: I need to extract the set of face cycles https://en.wikipedia.org/wiki/Cycle_basis#Face_cycles from a #planar graph, preferrably via Python (#networkx, #igraph, etc :networkx: :igraph:). I used networkx' minimum_cycle_basis() method so far, but I realized the minimum cycle basis is generally not the same as the face cycles. Does anybody know if there is a function for that in one of the common libraries? I want to avoid writing it myself if it's already out there.
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@danwwilson @lwpembleton #brms by @paul_buerkner has made Bayesian models incredibly fun and intuitive for me. I love the combination of well thought out defaults and API with a lot of depth and power, should you need it. Other than that, I think #lubridate needs some love! Oh and #igraph, which just works ™️ plus it's lovely descendant #tidygraph 🕸️
#packagelove -
Его величество Граф
Графы для меня особенная тема, в них есть нечто таинственное и мощное. В университете и в школе мы не проходили теорию графов. На работе никогда не произносили это слово. Но графы везде. И можно значительно упростить себе жизнь, если научиться видеть их и использовать многочисленные наработки по визуализации и алгоритмам. Я не буду рассказывать основы графов, они есть в Википедии . Цель статьи - поделиться с вами некоторыми случаями из моей практики, когда графы становились естественной частью какой-то задачи. Иногда без них задачу решить было невозможно. Иногда через них решение получалось более изящное. А иногда просто тяга к перфикционизму, графы это круто же) Ну что, поехали, будет интересно!
https://habr.com/ru/articles/828770/
#Графы #иерархии #деревья #networkx #igraph #графовые_алгоритмы
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Free Project for anyone. Someone should write modern and well documented FFI bindings to the igraph C library. There's a gem called steffi that uses FFI, but it lacks documentation of any kind. Someone also write C extensions to igraph, but they have *very* minimal documentation.
https://igraph.org/c/html/latest/
https://rubydoc.info/gems/igraph
https://rubydoc.info/gems/steffi
#ruby #ffi #igraph -
Work in progress.
#OpenScience research project that investigates the #walking sheds at the core of the 15-minute neighborhood, and their associated network attributes. Powered by #openstreetmap, #OpenData, and #rstats
@Belenmr @robinlovelace
@UrbanDemoghttps://github.com/paezha/15-minute-neighborhood
#ReproducibleResearch #transportation #ActiveTravel #Cities #Urbanism #Rstats #15MinCity #r5r #SimpleFeatures #igraph
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Calculating different #centrality measures for a street #network takes longer than expected.
#DegreeCentrality is calculated in a few milliseconds. But oh boy. #ClosenessCentrality and #BetweennessCentrality are proper whoppers. For a network of 65 000 nodes, we're talking about 2+ hour calculation times for the closeness centrality, not to mention the betweenness.
Apparently switching to #igraph would provide a speed boost over #networkx but the convenience of #osmnx has won me over.
-
Calculating different #centrality measures for a street #network takes longer than expected.
#DegreeCentrality is calculated in a few milliseconds. But oh boy. #ClosenessCentrality and #BetweennessCentrality are proper whoppers. For a network of 65 000 nodes, we're talking about 2+ hour calculation times for the closeness centrality, not to mention the betweenness.
Apparently switching to #igraph would provide a speed boost over #networkx but the convenience of #osmnx has won me over.
-
Calculating different #centrality measures for a street #network takes longer than expected.
#DegreeCentrality is calculated in a few milliseconds. But oh boy. #ClosenessCentrality and #BetweennessCentrality are proper whoppers. For a network of 65 000 nodes, we're talking about 2+ hour calculation times for the closeness centrality, not to mention the betweenness.
Apparently switching to #igraph would provide a speed boost over #networkx but the convenience of #osmnx has won me over.
-
Calculating different #centrality measures for a street #network takes longer than expected.
#DegreeCentrality is calculated in a few milliseconds. But oh boy. #ClosenessCentrality and #BetweennessCentrality are proper whoppers. For a network of 65 000 nodes, we're talking about 2+ hour calculation times for the closeness centrality, not to mention the betweenness.
Apparently switching to #igraph would provide a speed boost over #networkx but the convenience of #osmnx has won me over.
-
@danwwilson @lwpembleton #brms by @paul_buerkner has made Bayesian models incredibly fun and intuitive for me. I love the combination of well thought out defaults and API with a lot of depth and power, should you need it. Other than that, I think #lubridate needs some love! Oh and #igraph, which just works ™️ plus it's lovely descendant #tidygraph 🕸️
#packagelove -
@danwwilson @lwpembleton #brms by @paul_buerkner has made Bayesian models incredibly fun and intuitive for me. I love the combination of well thought out defaults and API with a lot of depth and power, should you need it. Other than that, I think #lubridate needs some love! Oh and #igraph, which just works ™️ plus it's lovely descendant #tidygraph 🕸️
#packagelove -
@danwwilson @lwpembleton #brms by @paul_buerkner has made Bayesian models incredibly fun and intuitive for me. I love the combination of well thought out defaults and API with a lot of depth and power, should you need it. Other than that, I think #lubridate needs some love! Oh and #igraph, which just works ™️ plus it's lovely descendant #tidygraph 🕸️
#packagelove -
@danwwilson @lwpembleton #brms by @paul_buerkner has made Bayesian models incredibly fun and intuitive for me. I love the combination of well thought out defaults and API with a lot of depth and power, should you need it. Other than that, I think #lubridate needs some love! Oh and #igraph, which just works ™️ plus it's lovely descendant #tidygraph 🕸️
#packagelove -
Work in progress.
#OpenScience research project that investigates the #walking sheds at the core of the 15-minute neighborhood, and their associated network attributes. Powered by #openstreetmap, #OpenData, and #rstats
@Belenmr @robinlovelace
@UrbanDemoghttps://github.com/paezha/15-minute-neighborhood
#ReproducibleResearch #transportation #ActiveTravel #Cities #Urbanism #Rstats #15MinCity #r5r #SimpleFeatures #igraph
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Work in progress.
#OpenScience research project that investigates the #walking sheds at the core of the 15-minute neighborhood, and their associated network attributes. Powered by #openstreetmap, #OpenData, and #rstats
@Belenmr @robinlovelace
@UrbanDemoghttps://github.com/paezha/15-minute-neighborhood
#ReproducibleResearch #transportation #ActiveTravel #Cities #Urbanism #Rstats #15MinCity #r5r #SimpleFeatures #igraph
-
Work in progress.
#OpenScience research project that investigates the #walking sheds at the core of the 15-minute neighborhood, and their associated network attributes. Powered by #openstreetmap, #OpenData, and #rstats
@Belenmr @robinlovelace
@UrbanDemoghttps://github.com/paezha/15-minute-neighborhood
#ReproducibleResearch #transportation #ActiveTravel #Cities #Urbanism #Rstats #15MinCity #r5r #SimpleFeatures #igraph
-
Work in progress.
#OpenScience research project that investigates the #walking sheds at the core of the 15-minute neighborhood, and their associated network attributes. Powered by #openstreetmap, #OpenData, and #rstats
@Belenmr @robinlovelace
@UrbanDemoghttps://github.com/paezha/15-minute-neighborhood
#ReproducibleResearch #transportation #ActiveTravel #Cities #Urbanism #Rstats #15MinCity #r5r #SimpleFeatures #igraph
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CW: Rstudio
Compiling the whole analysis of a paper I am working on in one RStudio Notebook, from data upload to #ANOVA using #BayesFactor package and plotting using #igraph and #ggplot2. Eventually would like to upload it to an open science data repository on submission. Have not done that before. Love working in #RStudio. #SocialNetworkAnalysis.
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CW: Rstudio
Compiling the whole analysis of a paper I am working on in one RStudio Notebook, from data upload to #ANOVA using #BayesFactor package and plotting using #igraph and #ggplot2. Eventually would like to upload it to an open science data repository on submission. Have not done that before. Love working in #RStudio. #SocialNetworkAnalysis.
