论文标题
使用张量分解的集合节点嵌入：深处小径上的案例研究
Ensemble Node Embeddings using Tensor Decomposition: A Case-Study on DeepWalk
论文作者
论文摘要
在过去的几年中，节点嵌入引起了人们越来越多的关注。在这种情况下，我们提出了一种新的集合节点嵌入方法，称为Tensemble2Vec，首先使用现有技术生成多个嵌入，并将它们作为tensor tensor分解模型的多视图数据输入，即parafac2，即学习了NODES的共享较低维度表示。与其他嵌入方法相反，我们的tensemble2Vec利用了来自不同方法的互补信息或使用不同的超参数的相同方法，这绕开了选择模型的挑战。使用现实世界数据进行的广泛测试验证了该方法的效率。
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.
