Author-topic evolution analysis using three-way non-negative Paratucker
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Using Web Clustering for Web Communities Mining and Analysis
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Using DEDICOM for completely unsupervised part-of-speech tagging
UMSLLS '09 Proceedings of the Workshop on Unsupervised and Minimally Supervised Learning of Lexical Semantics
Bayesian block modelling for weighted networks
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
Factorizing YAGO: scalable machine learning for linked data
Proceedings of the 21st international conference on World Wide Web
Tensor clustering via adaptive subspace iteration
Intelligent Data Analysis
Hi-index | 0.00 |
ASALSAN is a new algorithm for computing three-way DEDICOM, which is a linear algebra model for analyzing intrinsically asymmetric relationships, such as trade among nations or the exchange of emails among individuals, that incorporates a third mode of the data, such as time. ASALSAN is unique because it enables computing the three-way DEDICOM model on large, sparse data. A nonnegative version of ASALSAN is described as well. When we apply these techniques to adjacency arrays arising from directed graphs with edges labeled by time, we obtain a smaller graph on latent semantic dimensions and gain additional information about their changing relationships over time. We demonstrate these techniques on international trade data and the Enron email corpus to uncover latent components and their transient behavior. The mixture of roles assigned to individuals by ASALSAN showed strong correspondence with known job classifications and revealed the patterns of communication between these roles. Changes in the communication pattern over time, e.g., between top executives and the legal department, were also apparent in the solutions.