论文标题
T- HOP：图形卷积网络中路径的张量表示
T- Hop: Tensor representation of paths in graph convolutional networks
论文作者
论文摘要
我们描述了一种将图形信息编码为3D张量的方法。我们显示了引入的路径表示方案与动力邻接矩阵之间的联系。为了减轻与3-D张量合作的大量计算需求，我们建议在张量的深度轴上施加尺寸降低。然后，我们通过将其注入已建立的图形卷积网络框架（如Mixhop）中来描述我们还原的3-D矩阵可以将其分解为合理的图形卷积层。
We describe a method for encoding path information in graphs into a 3-d tensor. We show a connection between the introduced path representation scheme and powered adjacency matrices. To alleviate the heavy computational demands of working with the 3-d tensor, we propose to apply dimensionality reduction on the depth axis of the tensor. We then describe our the reduced 3-d matrix can be parlayed into a plausible graph convolutional layer, by infusing it into an established graph convolutional network framework such as MixHop.
