Fedivertex: A graph dataset based on decentralized Social Media

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.