TensorCast: Forecasting with Context using Coupled TensorsMiguel Araujo, Pedro Ribeiro and Christos Faloutsos2017 |
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.
Miguel Araujo, Pedro Ribeiro and Christos Faloutsos . TensorCast: Forecasting with Context using Coupled Tensors. (Best Paper Award) Proceedings of the IEEE International Conference on Data Mining (ICDM), pp. 71-80, IEEE, New Orleans, USA, November, 2017.
@inproceedings{ribeiro-ICDM2017, author = {Miguel Araujo and Pedro Ribeiro and Christos Faloutsos }, title = {TensorCast: Forecasting with Context using Coupled Tensors}, doi = {10.1109/ICDM.2017.16}, booktitle = {IEEE International Conference on Data Mining}, pages = {71-80}, publisher = {IEEE}, month = {November}, year = {2017} }