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Node embeddings have been attracting increasing attention during the pastyears. In this context, we propose a new ensemble node embedding approach,called TenSemble2Vec, by first generating multiple embeddings using theexisting techniques and taking them as multiview data input of the state-of-arttensor decomposition model namely PARAFAC2 to learn the sharedlower-dimensional representations of the nodes. Contrary to other embeddingmethods, our TenSemble2Vec takes advantage of the complementary informationfrom different methods or the same method with different hyper-parameters,which bypasses the challenge of choosing models. Extensive tests usingreal-world data validates the efficiency of the proposed method.

Ensemble Node Embeddings using Tensor Decomposition: A Case-Study on DeepWalk