Yitian Zhang

yitian.zhang@mila.quebec

Doctorat

Superviseur⋅e principal⋅e

Mark Coates

Github

Publications

CKGConv: General Graph Convolution with Continuous Kernels

Liheng Ma

Soumyasundar Pal

Yitian Zhang

Jiaming Zhou

Yingxue Zhang

Mark Coates

The existing definitions of graph convolution, either from spatial or spectral perspectives, are inflexible and not unified. Defining a gene… (voir plus)ral convolution operator in the graph domain is challenging due to the lack of canonical coordinates, the presence of irregular structures, and the properties of graph symmetries. In this work, we propose a novel graph convolution framework by parameterizing the kernels as continuous functions of pseudo-coordinates derived via graph positional encoding. We name this Continuous Kernel Graph Convolution (CKGConv). Theoretically, we demonstrate that CKGConv is flexible and expressive. CKGConv encompasses many existing graph convolutions, and exhibits the same expressiveness as graph transformers in terms of distinguishing non-isomorphic graphs. Empirically, we show that CKGConv-based Networks outperform existing graph convolutional networks and perform comparably to the best graph transformers across a variety of graph datasets.

2024-05-01

ICML.cc/2024/Conference (poster)

doi.org

openreview.net

Multi-resolution Time-Series Transformer for Long-term Forecasting

Yitian Zhang

Liheng Ma

Soumyasundar Pal

Yingxue Zhang

Mark Coates

2024-04-18

Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (publié)