<?xml version="1.0" encoding="UTF-8"?>

<modsCollection xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.loc.gov/mods/v3" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-3.xsd">
<mods version="3.3">

<genre>conference paper</genre>

<titleInfo><title>Fedivertex: A graph dataset based on decentralized Social Media</title></titleInfo>


<note type="publicationStatus">accepted</note>



<name type="personal">
  <namePart type="given">Marc</namePart>
  <namePart type="family">Damie</namePart>
  <role><roleTerm type="text">author</roleTerm> </role></name>
<name type="personal">
  <namePart type="given">Edwige Audrey Lucienne</namePart>
  <namePart type="family">Cyffers</namePart>
  <role><roleTerm type="text">author</roleTerm> </role><identifier type="local">20d4c299-977a-11ef-ae55-98b15ac64a57</identifier></name>







<name type="corporate">
  <namePart></namePart>
  <identifier type="local">ChLa</identifier>
  <role>
    <roleTerm type="text">department</roleTerm>
  </role>
</name>



<name type="conference">
  <namePart>WWW: Web Conference</namePart>
</name>






<abstract lang="eng">Social network graphs are central to graph learning research, serving as standard benchmarks for algorithm evaluation. However, existing datasets focus mainly on mainstream social media platforms whose structures are shaped notably by algorithmic recommendations. This raises an important question: would alternative, decentralized social networks exhibit different properties? We address this by studying the Fediverse; a collection of decentralized social networks (such as Mastodon and Lemmy). These platforms differ fundamentally from for-profit social media, notably in decentralization and absence of recommendation algorithms, which may yield distinct graph structures. We introduce Fedivertex, a dataset of over 400 graphs from seven decentralized networks, collected weekly over six months. The dataset, released with a companion Python package to facilitate its use, supports research on temporal and structural aspects of decentralized social networks. In particular, we benchmark applications to decentralized machine learning and community detection.</abstract>

<originInfo><publisher>ACM</publisher><dateIssued encoding="w3cdtf">2026</dateIssued><place><placeTerm type="text">Dubai</placeTerm></place>
</originInfo>
<language><languageTerm authority="iso639-2b" type="code">eng</languageTerm>
</language>



<relatedItem type="host"><titleInfo><title>2026 Proceedings of the ACM Web Conference 2026</title></titleInfo>
  <identifier type="isbn">9798400723070</identifier><identifier type="doi">10.1145/3774904.3792868</identifier>
<part><extent unit="pages">8393-8396</extent>
</part>
</relatedItem>


<extension>
<bibliographicCitation>
<short>M. Damie, E.A.L. Cyffers, in:, 2026 Proceedings of the ACM Web Conference 2026, ACM, n.d., pp. 8393–8396.</short>
<ista>Damie M, Cyffers EAL. Fedivertex: A graph dataset based on decentralized Social Media. 2026 Proceedings of the ACM Web Conference 2026. WWW: Web Conference, 8393–8396.</ista>
<ama>Damie M, Cyffers EAL. Fedivertex: A graph dataset based on decentralized Social Media. In: &lt;i&gt;2026 Proceedings of the ACM Web Conference 2026&lt;/i&gt;. ACM; :8393-8396. doi:&lt;a href=&quot;https://doi.org/10.1145/3774904.3792868&quot;&gt;10.1145/3774904.3792868&lt;/a&gt;</ama>
<mla>Damie, Marc, and Edwige Audrey Lucienne Cyffers. “Fedivertex: A Graph Dataset Based on Decentralized Social Media.” &lt;i&gt;2026 Proceedings of the ACM Web Conference 2026&lt;/i&gt;, ACM, pp. 8393–96, doi:&lt;a href=&quot;https://doi.org/10.1145/3774904.3792868&quot;&gt;10.1145/3774904.3792868&lt;/a&gt;.</mla>
<ieee>M. Damie and E. A. L. Cyffers, “Fedivertex: A graph dataset based on decentralized Social Media,” in &lt;i&gt;2026 Proceedings of the ACM Web Conference 2026&lt;/i&gt;, Dubai, pp. 8393–8396.</ieee>
<chicago>Damie, Marc, and Edwige Audrey Lucienne Cyffers. “Fedivertex: A Graph Dataset Based on Decentralized Social Media.” In &lt;i&gt;2026 Proceedings of the ACM Web Conference 2026&lt;/i&gt;, 8393–96. ACM, n.d. &lt;a href=&quot;https://doi.org/10.1145/3774904.3792868&quot;&gt;https://doi.org/10.1145/3774904.3792868&lt;/a&gt;.</chicago>
<apa>Damie, M., &amp;#38; Cyffers, E. A. L. (n.d.). Fedivertex: A graph dataset based on decentralized Social Media. In &lt;i&gt;2026 Proceedings of the ACM Web Conference 2026&lt;/i&gt; (pp. 8393–8396). Dubai: ACM. &lt;a href=&quot;https://doi.org/10.1145/3774904.3792868&quot;&gt;https://doi.org/10.1145/3774904.3792868&lt;/a&gt;</apa>
</bibliographicCitation>
</extension>
<recordInfo><recordIdentifier>21916</recordIdentifier><recordCreationDate encoding="w3cdtf">2026-05-24T22:01:32Z</recordCreationDate><recordChangeDate encoding="w3cdtf">2026-06-03T05:40:18Z</recordChangeDate>
</recordInfo>
</mods>
</modsCollection>
