A Simple Hypergraph Kernel Convolution based on Discounted Markov Diffusion Process. (arXiv:2210.16884v1 [cs.LG])

Kernels on discrete structures evaluate pairwise similarities between objects
which capture semantics and inherent topology information. Existing kernels on
discrete structures are only developed by topology information(such as
adjacency matrix of graphs), without considering original attributes of
objects. This paper proposes a two-phase paradigm to aggregate comprehensive
information on discrete structures leading to a Discount Markov Diffusion
Learnable Kernel (DMDLK). Specifically, based on the underlying projection of
DMDLK, we design a Simple Hypergraph Kernel Convolution (SHKC) for hidden
representation of …

arxiv convolution diffusion hypergraph kernel markov process