Hierarchical Clustering and Signal Denoising on Digraphs
In this paper, we propose a representation of a digraph (directed graph) as a Hermitian matrix derived from its adjacency matrix. This representation characterizes both the connectivity and the edge orientation of the digraph. Based on the spectral decomposition of the Hermitian matrix, a digraph clustering algorithm with $k$-means is introduced to produce a partition on the graph. Applying this algorithm (bottom-up) recursively to a digraph with partially labeled vertices yields a spectral hierarchical digraph clustering (\myproj) algorithm that produces consistent nested partitions of the digraph, or equivalently, a tree structure. Furthermore, based on the in-degree and out-degree of e...
| Authors | Yi Wang, Sippanon Kitimoon, Hrushikesh N. Mhaskar et al. |
|---|---|
| Primary category | eess.SP |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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