Local Hyper-Flow Diffusion (HFD) gives an edge-size-independent Cheeger-type
guarantee for seeded clustering in general submodular hypergraphs, but existing
HFD solvers do not keep intermediate computation local at every iteration. We …

Clustering algorithms are essential tools for exploring data structures and have
applications in various fields of knowledge. Among them, spectral clustering, based
on graph theory, stands out for its performance on non-convex data and has been …

Convolutions have successfully transitioned from image processing to the complex
realm of non-Euclidean higher-order domains, particularly in hypergraphs. Despite
the success in convolution, the exploration of a popular architecture named U-Net …

Large language models (LLMs) have recently shown strong potential in modeling
relational structures. However, existing approaches remain fundamentally graph-
centric: they focus on processing pairwise graph structures into tokens that LLMs can …