TENSORCAST: forecasting and mining with coupled tensors

• Tipo: Artigo em Revista Científica Internacional
• Data: 2019

Autores

araujo, mr (Autor) FCUP; Pedro Ribeiro (Autor) FCUP; Song, HA (Autor) Outra; Faloutsos, C (Autor) Outra

Revista

Título: Knowledge and Information Systems

Vol. 59

Páginas: 497-522

ISSN: 0219-1377

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 8 Citações

Scopus - 7 Citações

Outras Informações

ID Authenticus: P-00Q-DCJ

DOI: 10.1007/s10115-018-1223-9

Abstract (EN): Given an heterogeneous social network, can we forecast its future? Can we predict who will start using a given hashtag on twitter? Can we leverage side information, such as who retweets or follows whom, to improve our membership forecasts? We present TENSORCAST, a novel method that forecasts time-evolving networks more accurately than current state-of-the-art methods by incorporating multiple data sources in coupled tensors. TENSORCAST is (a) scalable, being linearithmic on the number of connections; (b) effective, achieving over 20% improved precision on top-1000 forecasts of community members; (c) general, being applicable to data sources with different structure. We run our method on multiple real-world networks, including DBLP, epidemiology data, power grid data, and a Twitter temporal network with over 310 million nonzeros, where we predict the evolution of the activity of the use of political hashtags.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 26