TensorCast: forecasting and mining with coupled tensors Miguel Araujo, Pedro Ribeiro, Hyun Ah Song and Christos Faloutsos 2019

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.

Keywords

Time-evolving network; Coupled tensor; Forecasting

Digital Object Identifier (DOI)

doi 10.1007/s10115-018-1223-9

Publication in PDF format

pdf Download PDF

Software

software TensorCast

Journal/Conference/Book

Knowledge and Information Systems

Reference (text)

Miguel Araujo, Pedro Ribeiro, Hyun Ah Song and Christos Faloutsos. TensorCast: forecasting and mining with coupled tensors. In Knowledge and Information Systems, Vol. 59(3), pp. 497-522, Springer, June, 2019.

Bibtex

@article{ribeiro-KAIS2018,
  author = {Miguel Araujo and  Pedro Ribeiro and  Hyun Ah Song and Christos Faloutsos},
  title = {TensorCast: forecasting and mining with coupled tensors},
  doi = {10.1007/s10115-018-1223-9},
  journal = {Knowledge and Information Systems},
  volume = {59},
  issue = {3},
  pages = {497-522},
  publisher = {Springer},
  month = {June},
  year = {2019}
}