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- Title
TENSORCAST: forecasting and mining with coupled tensors.
- Authors
Araujo, Miguel; Ribeiro, Pedro; Song, Hyun Ah; Faloutsos, Christos
- Abstract
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.
- Subjects
TIME-varying networks; ELECTRIC power distribution grids; POLITICAL participation; SOCIAL networks; TAGS (Metadata)
- Publication
Knowledge & Information Systems, 2019, Vol 59, Issue 3, p497
- ISSN
0219-1377
- Publication type
Article
- DOI
10.1007/s10115-018-1223-9