Chapitre D'ouvrage Année : 2024

Scalable Algorithms to Measure User Influence in Social Networks

Nouamane Arhachoui
Esteban Bautista
  • Fonction : Auteur
  • PersonId : 1195679
Maximilien Danisch
  • Fonction : Auteur
  • PersonId : 940804
  • IdRef : 188447407
Lionel Tabourier

Résumé

Measuring user influence in social networks is crucial for a variety of applications. While traditional centrality metrics evaluate structural graph importance, a more recent metric known as the ψ-score takes into account users' posting and re-posting activities to provide richer information. The ψ-score is a powerful tool that generalizes PageRank for non-homogeneous node activity. However, for large datasets with N users, it becomes computationally expensive, requiring solving N linear systems of N equations. To tackle this issue, we propose three new scalable algorithms that can quickly approximate the ψ-score. The Power-ψ and Push-ψ algorithms are based on a novel equation that shows it is sufficient to solve one system of equations of size N to calculate the ψ-score. These algorithms take advantage of the fact that the solution of such a system can be recursively and distributedly approximated. Consequently, the ψ-score, which summarizes the nodes' structural and behavioral information, can be computed as quickly as PageRank. The third proposed algorithm is Push-NF. Despite aiming to solve all N systems to extract additional information on the information dynamics, it still manages to converge to the accurate user ranking faster than the current state-of-the-art alternative. To validate the effectiveness of our proposed algorithms, we release them as an open-source Python library and test them on various real-world datasets.

Fichier principal
Vignette du fichier
LNSN.pdf (680.54 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
licence

Dates et versions

hal-04317506 , version 1 (01-12-2023)
hal-04317506 , version 2 (18-05-2026)

Licence

Identifiants

Citer

Nouamane Arhachoui, Esteban Bautista, Maximilien Danisch, Anastasios Giovanidis, Lionel Tabourier. Scalable Algorithms to Measure User Influence in Social Networks. Mehmet Kaya; Sleiman Alhajj; Kashfia Sailunaz; Min-Yuh Day. Social Network Analysis and Mining Applications in Healthcare and Anomaly Detection., Springer Nature Switzerland, pp.63-92, 2024, Lecture Notes in Social Networks, 978-3-031-75204-9. ⟨10.1007/978-3-031-75204-9_3⟩. ⟨hal-04317506v2⟩
207 Consultations
259 Téléchargements

Altmetric

Partager

  • More