TensorCast : forecasting and mining with coupled tensors
Knowledge and Information Systems(2018)
摘要
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.
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关键词
Time-evolving network,Coupled tensor,Forecasting
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