TensorCast: Forecasting Time-Evolving Networks with Contextual Information

TensorCast: Forecasting Time-Evolving Networks with Contextual Information

Miguel Araújo, Pedro Ribeiro, Christos Faloutsos

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Best Sister Conferences. Pages 5199-5203. https://doi.org/10.24963/ijcai.2018/721

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.
Keywords:
Machine Learning: Data Mining
Machine Learning: Time-series;Data Streams