TensorCast: Forecasting with Context using Coupled Tensors

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

araujo, mr (Author) Other; Pedro Ribeiro (Author) FCUP; Faloutsos, C (Author) Other

Conference proceedings International

Title: 2017 17TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM)

Pages: 71-80

17th IEEE International Conference on Data Mining (ICDMW)

New Orleans, LA, NOV 18-21, 2017

Indexing

ISI Web of Knowledge - 25 Citations

Scopus - 32 Citations

Other information

Authenticus ID: P-00N-RK5

DOI: 10.1109/icdm.2017.16

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 and a Twitter temporal network with over 310 million non-zeros, where we predict the evolution of the activity of the use of political hashtags.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10