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

arXiv: https://arxiv.org/abs/2204.04983 · HTML: https://mlsanity.com/paper/2204.04983/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 5 references that are in the map.
- First posted on arXiv 11 Apr 2022.

## Builds on (most cited first, 3 of 3)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing](https://mlsanity.com/paper/1905.00067/) (2019)
- [Power up! Robust Graph Convolutional Network via Graph Powering](https://mlsanity.com/paper/1905.10029/) (2019)

## Abstract (arXiv)

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.