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#networkx

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Timothy Shortell<p>I'm going to have to work on the quality of my corpus network graphs. <a href="https://sciences.social/tags/ComputationalSocialScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ComputationalSocialScience</span></a> <a href="https://sciences.social/tags/SociologyOfReligion" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SociologyOfReligion</span></a> <a href="https://sciences.social/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Python</span></a> <a href="https://sciences.social/tags/BagOfWords" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BagOfWords</span></a> <a href="https://sciences.social/tags/Gensim" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>Gensim</span></a> <a href="https://sciences.social/tags/NetworkX" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NetworkX</span></a></p>
Tuomas Väisänen 📼🧟‍♂️<p>I don't do a lot of <a href="https://vis.social/tags/NetworkAnalysis" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NetworkAnalysis</span></a> in my work, but there are many in <span class="h-card" translate="no"><a href="https://mastodon.online/@digigeolab" class="u-url mention" rel="nofollow noopener" target="_blank">@<span>digigeolab</span></a></span> who do, and presumably here as well.</p><p>I know <a href="https://vis.social/tags/NetworkX" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>NetworkX</span></a> is a commonly used Python library for such analysis, but it is a bit slow. However, there is a new spin of it called <a href="https://vis.social/tags/RustworkX" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>RustworkX</span></a> written in Rust, which makes it blisteringly fast.</p><p>Here are some benchmarks: <a href="https://www.rustworkx.org/dev/benchmarks.html" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">rustworkx.org/dev/benchmarks.h</span><span class="invisible">tml</span></a></p><p>Has anyone tried it? Any opinions?</p>
Michael Szell<p>Please help - <a href="https://datasci.social/tags/networkScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>networkScience</span></a> question: I need to extract the set of face cycles <a href="https://en.wikipedia.org/wiki/Cycle_basis#Face_cycles" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">en.wikipedia.org/wiki/Cycle_ba</span><span class="invisible">sis#Face_cycles</span></a> from a <a href="https://datasci.social/tags/planar" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>planar</span></a> graph, preferrably via Python (<a href="https://datasci.social/tags/networkx" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>networkx</span></a>, <a href="https://datasci.social/tags/igraph" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>igraph</span></a>, 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.</p>
Tuomas Väisänen 📼🧟‍♂️<p>Calculating different <a href="https://vis.social/tags/centrality" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>centrality</span></a> measures for a street <a href="https://vis.social/tags/network" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>network</span></a> takes longer than expected.</p><p><a href="https://vis.social/tags/DegreeCentrality" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DegreeCentrality</span></a> is calculated in a few milliseconds. But oh boy. <a href="https://vis.social/tags/ClosenessCentrality" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ClosenessCentrality</span></a> and <a href="https://vis.social/tags/BetweennessCentrality" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>BetweennessCentrality</span></a> 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.</p><p>Apparently switching to <a href="https://vis.social/tags/igraph" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>igraph</span></a> would provide a speed boost over <a href="https://vis.social/tags/networkx" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>networkx</span></a> but the convenience of <a href="https://vis.social/tags/osmnx" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>osmnx</span></a> has won me over.</p>
Vincent 🦾 🤖<p>et pour le code source c'est par ici: <a href="https://github.com/vcrocher/genealogieESR" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">github.com/vcrocher/genealogie</span><span class="invisible">ESR</span></a></p><p><a href="https://scicomm.xyz/tags/esr" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>esr</span></a> <a href="https://scicomm.xyz/tags/python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>python</span></a> <a href="https://scicomm.xyz/tags/networkx" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>networkx</span></a></p>
Alexandre Dulaunoy<p>For the ones using the incredible NetworkX library there is a new backend with support for GPU-accelerated capabilities. nx-cugraph seems to support out of the box a majority of the functions. </p><p><a href="https://infosec.exchange/tags/opensource" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>opensource</span></a> <a href="https://infosec.exchange/tags/networkx" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>networkx</span></a> <a href="https://infosec.exchange/tags/gpu" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>gpu</span></a> </p><p><a href="https://github.com/rapidsai/cugraph#news" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">github.com/rapidsai/cugraph#ne</span><span class="invisible">ws</span></a></p>
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