{
  "annotation_id": "24d8b016-6d2f-4103-bfbf-496a39db2d24",
  "date_created": "2026-02-17T05:53:20.470000Z",
  "date_modified": "2026-02-17T05:53:20.470000Z",
  "file_hash": "8b04d8f9239d9a98e8bc934a1d07617fe6318ff29efa41d611a0d73b65233280",
  "private": false,
  "record": {
    "abstract": "We propose graph canonical coherence analysis (gCChA), a novel framework that extends canonical correlation analysis to multivariate graph signals in the graph frequency domain. The proposed method addresses challenges posed by the inherent features of graphs: discreteness, finiteness, and irregularity. It identifies pairs of canonical graph signals that maximize their coherence, enabling the exploration of relationships between two sets of graph signals from a spectral perspective. This framework shows how these relationships change across different structural scales of the graph. We demonstrate the usefulness of this method through applications to economic and energy datasets of G20 countries and the USPS handwritten digit dataset.",
    "arxiv_id": "2601.09038",
    "authors": [
      "Kyusoon Kim",
      "Hee-Seok Oh"
    ],
    "categories": [
      "stat.ME"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Graph Canonical Coherence Analysis",
    "url": "https://arxiv.org/abs/2601.09038",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "e8978fe0-3c08-4539-800a-1bfc8bc104e0",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}