TensorCast: Forecasting Time-Evolving Networks with Contextual Information
Abstract
Can we forecast future connections in a social network? Can we predict who will start using a given hashtag in Twitter, leveraging contextual information such as who follows or retweets whom to improve our predictions? In this paper we present an abridged report of TensorCast, an award winning method for forecasting time-evolving networks, that uses coupled tensors to incorporate multiple information sources. TensorCast is scalable (linearithmic on the number of connections), effective (more precise than competing methods) and general (applicable to any data source representable by a tensor). We also showcase our method when applied to forecast two large scale heterogeneous real world temporal networks, namely Twitter and DBLP.
Cite
Text
Araujo et al. "TensorCast: Forecasting Time-Evolving Networks with Contextual Information." International Joint Conference on Artificial Intelligence, 2018. doi:10.24963/IJCAI.2018/721Markdown
[Araujo et al. "TensorCast: Forecasting Time-Evolving Networks with Contextual Information." International Joint Conference on Artificial Intelligence, 2018.](https://mlanthology.org/ijcai/2018/araujo2018ijcai-tensorcast/) doi:10.24963/IJCAI.2018/721BibTeX
@inproceedings{araujo2018ijcai-tensorcast,
title = {{TensorCast: Forecasting Time-Evolving Networks with Contextual Information}},
author = {Araujo, Miguel and Ribeiro, Pedro Manuel Pinto and Faloutsos, Christos},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2018},
pages = {5199-5203},
doi = {10.24963/IJCAI.2018/721},
url = {https://mlanthology.org/ijcai/2018/araujo2018ijcai-tensorcast/}
}