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  <section id="topox">
<h1>🌐 TopoX 🍩<a class="headerlink" href="#topox" title="Link to this heading">#</a></h1>
<p><cite>TopoX</cite> are a set of Python packages for meeting diverse computational needs on topological domains. Topological domains are the natural mathematical structures representing relations between the components of a dataset.</p>
<figure class="align-default">
<a class="with-shadow reference internal image-reference" href="https://user-images.githubusercontent.com/8267869/234068354-af9480f1-1d18-4914-92f1-916d9093e44d.png"><img alt="natural shapes" class="with-shadow" src="https://user-images.githubusercontent.com/8267869/234068354-af9480f1-1d18-4914-92f1-916d9093e44d.png" style="width: 1000px;" />
</a>
</figure>
<p>Many natural systems as diverse as social networks and proteins are characterized by relational structure. This is the structure of interactions between
components in the system, such as social interactions between individuals or electrostatic interactions between atoms.</p>
<p><cite>TopoX</cite> provides a unifying interface to compute and learn with such relational data.</p>
<section id="scope-and-functionality">
<h2>🎯 Scope and functionality<a class="headerlink" href="#scope-and-functionality" title="Link to this heading">#</a></h2>
<p><cite>TopoX</cite> are a set of packages for computing with topological domains and studying their properties.</p>
<p>It consists of three different packages:</p>
<ul class="simple">
<li><dl class="simple">
<dt><cite>TopoNetX</cite> (TNX) which introduces a NetworkX like interface for topological domains.</dt><dd><p>Jump to detailed documentation for <a class="reference external" href="https://pyt-team.github.io/toponetx">TopoNetX</a>.</p>
</dd>
</dl>
</li>
<li><dl class="simple">
<dt><cite>TopoModelX</cite> (TMX) which implements deep learning on topological domains.</dt><dd><p>Jump to detailed documentation for the <a class="reference external" href="https://pyt-team.github.io/topomodelx">TopoModelX</a>.</p>
</dd>
</dl>
</li>
<li><dl class="simple">
<dt><cite>TopoEmbedX</cite> (TEX) which provides methods for generating embeddings on topological domains.</dt><dd><p>Jump to detailed documentation for the <a class="reference external" href="https://pyt-team.github.io/topoembedx">TopoEmbedX</a>.</p>
</dd>
</dl>
</li>
</ul>
</section>
<section id="references">
<h2>🔍 References<a class="headerlink" href="#references" title="Link to this heading">#</a></h2>
<p>To learn more about how topological domains are used in deep learning:</p>
<ul class="simple">
<li><p>Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, Nina Miolane, Aldo Guzmán-Sáenz, Karthikeyan Natesan Ramamurthy, Tolga Birdal, Tamal K. Dey, Soham Mukherjee, Shreyas N. Samaga, Neal Livesay, Robin Walters, Paul Rosen, Michael T. Schaub. <a class="reference external" href="https://arxiv.org/abs/2206.00606">Topological Deep Learning: Going Beyond Graph Data</a>.</p></li>
</ul>
<div class="highlight-BibTeX notranslate"><div class="highlight"><pre><span></span><span class="nc">@misc</span><span class="p">{</span><span class="nl">hajij2023topological</span><span class="p">,</span>
<span class="w">      </span><span class="na">title</span><span class="p">=</span><span class="s">{Topological Deep Learning: Going Beyond Graph Data}</span><span class="p">,</span>
<span class="w">      </span><span class="na">author</span><span class="p">=</span><span class="s">{Mustafa Hajij and Ghada Zamzmi and Theodore Papamarkou and Nina Miolane and Aldo Guzmán-Sáenz and Karthikeyan Natesan Ramamurthy and Tolga Birdal and Tamal K. Dey and Soham Mukherjee and Shreyas N. Samaga and Neal Livesay and Robin Walters and Paul Rosen and Michael T. Schaub}</span><span class="p">,</span>
<span class="w">      </span><span class="na">year</span><span class="p">=</span><span class="s">{2023}</span><span class="p">,</span>
<span class="w">      </span><span class="na">eprint</span><span class="p">=</span><span class="s">{2206.00606}</span><span class="p">,</span>
<span class="w">      </span><span class="na">archivePrefix</span><span class="p">=</span><span class="s">{arXiv}</span><span class="p">,</span>
<span class="w">      </span><span class="na">primaryClass</span><span class="p">=</span><span class="s">{cs.LG}</span>
<span class="p">}</span>
</pre></div>
</div>
<ul class="simple">
<li><p>Mathilde Papillon, Sophia Sanborn, Mustafa Hajij, Nina Miolane. <a class="reference external" href="https://arxiv.org/pdf/2304.10031.pdf">Architectures of Topological Deep Learning: A Survey on Topological Neural Networks</a>.</p></li>
</ul>
<div class="highlight-BibTeX notranslate"><div class="highlight"><pre><span></span><span class="nc">@misc</span><span class="p">{</span><span class="nl">papillon2023architectures</span><span class="p">,</span>
<span class="w">      </span><span class="na">title</span><span class="p">=</span><span class="s">{Architectures of Topological Deep Learning: A Survey on Topological Neural Networks}</span><span class="p">,</span>
<span class="w">      </span><span class="na">author</span><span class="p">=</span><span class="s">{Mathilde Papillon and Sophia Sanborn and Mustafa Hajij and Nina Miolane}</span><span class="p">,</span>
<span class="w">      </span><span class="na">year</span><span class="p">=</span><span class="s">{2023}</span><span class="p">,</span>
<span class="w">      </span><span class="na">eprint</span><span class="p">=</span><span class="s">{2304.10031}</span><span class="p">,</span>
<span class="w">      </span><span class="na">archivePrefix</span><span class="p">=</span><span class="s">{arXiv}</span><span class="p">,</span>
<span class="w">      </span><span class="na">primaryClass</span><span class="p">=</span><span class="s">{cs.LG}</span>
<span class="p">}</span>
</pre></div>
</div>
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