T- Hop: Tensor representation of paths in graph convolutional networks

Abdulrahman Ibraheem

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arXiv · 2204.04983

T- Hop: Tensor representation of paths in graph convolutional networks

Abdulrahman Ibraheem

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Abstract

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.

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BibTeX RIS

Abdulrahman Ibraheem. 2022-04-11. T- Hop: Tensor representation of paths in graph convolutional networks. https://arxiv.org/abs/2204.04983

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