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 the complementary information from different methods or the same method with different hyper-parameters, which bypasses the challenge of choosing models. Extensive tests using real-world data validates the efficiency of the proposed method.
翻译:过去几年来,节点嵌入一直引起越来越多的注意。 在这方面,我们提议一种新的混合节点嵌入方法,称为“TenSemble2Vec”,首先利用现有技术生成多个嵌入器,并将之作为最新高压分解模型(即PARAFAC2)的多视图数据输入,以学习节点的共享低维表示。 与其他嵌入方法相反,我们的TenSemble2Vec利用了不同方法或相同方法的补充信息,而不同超常参数则绕过选择模型的挑战。使用现实世界数据的广泛测试验证了拟议方法的效率。