TensorCast: Forecasting Time-Evolving Networks with Contextual Information Miguel Araujo, Pedro Ribeiro and Christos Faloutsos 2018

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 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.

Digital Object Identifier (DOI)

doi 10.24963/ijcai.2018/721

Publication in PDF format

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Software

software TensorCast

Journal/Conference/Book

27th International Joint Conference on Artificial Intelligence - Sister Conferences Best Papers

Reference (text)

Miguel Araujo, Pedro Ribeiro and Christos Faloutsos. TensorCast: Forecasting Time-Evolving Networks with Contextual Information. Proceedings of the 27th International Joint Conference on Artificial Intelligence - Sister Conferences Best Papers (IJCAI), pp. 5199-5203, Stockholm, Sweden, July, 2018.

Bibtex

@inproceedings{ribeiro-IJCAI2018,
  author = {Miguel Araujo and  Pedro Ribeiro and Christos Faloutsos},
  title = {TensorCast: Forecasting Time-Evolving Networks with Contextual Information},
  doi = {10.24963/ijcai.2018/721},
  booktitle = {27th International Joint Conference on Artificial Intelligence - Sister Conferences Best Papers},
  pages = {5199-5203},
  month = {July},
  year = {2018}
}