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2020 International Conference on Data Mining Workshops (ICDMW)
Node embeddings have been attracting increasing attention during the past years. In this context, we propose a new ensemble node embedding approach, called TENSEMBLE2VEC, by first generating multiple embeddings using the existing techniques and taking them as multiview data input of the state-of-art tensor decomposition model namely PARAFAC2 to learn the shared lower-dimensional representations of the nodes. Contrary to other embedding methods, our TENSEMBLE2VEC takes advantage of thedoi:10.1109/icdmw51313.2020.00080 fatcat:zkxi54u26raapc5ydnxws6ibfi