---
res:
  bibo_abstract:
  - '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.@eng'
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Marc
      foaf_name: Damie, Marc
      foaf_surname: Damie
  - foaf_Person:
      foaf_givenName: Edwige Audrey Lucienne
      foaf_name: Cyffers, Edwige Audrey Lucienne
      foaf_surname: Cyffers
      foaf_workInfoHomepage: http://www.librecat.org/personId=20d4c299-977a-11ef-ae55-98b15ac64a57
  bibo_doi: 10.1145/3774904.3792868
  dct_date: 2026^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/9798400723070
  dct_language: eng
  dct_publisher: ACM@
  dct_title: 'Fedivertex: A graph dataset based on decentralized Social Media@'
...