DyNMF: Role analytics in dynamic social networks

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Roles of nodes in a social network (SN) represent their functions, responsibilities or behaviors within the SN. Roles typically evolve over time, making role analytics a challenging problem. Previous studies either neglect role transition analysis or perform role discovery and role transition learning separately, leading to inefficiencies and limited transition analysis. We propose a novel dynamic non-negative matrix factorization (DyNMF) approach to simultaneously discover roles and learn role transitions. DyNMF explicitly models temporal information by introducing a role transition matrix and clusters nodes in SNs from two views: the current view and the historical view. The current view captures structural information from the current SN snapshot and the historical view captures role transitions by looking at roles in past SN snapshots. DyNMF efficiently provides more effective analytics capabilities, regularizing roles by temporal smoothness of role transitions and reducing uncertainties and inconsistencies between snapshots. Experiments on both synthetic and real-world SNs demonstrate the advantages of DyNMF in discovering and predicting roles and role transitions.

Originele taal-2Engels
TitelProceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018
RedacteurenJerome Lang
Plaats van productieCalifornia
UitgeverijInternational Joint Conferences on Artificial Intelligence
Aantal pagina's7
ISBN van elektronische versie9780999241127
StatusGepubliceerd - 1 jan 2018
Evenement27th International Joint Conference on Artificial Intelligence, IJCAI 2018 - Stockholm, Zweden
Duur: 13 jul 201819 jul 2018


Congres27th International Joint Conference on Artificial Intelligence, IJCAI 2018

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