{
  "abstract": "Dynamic networks are being applied in many domains, from social media to logistics systems, each with their own set of special characteristics. A model employed on this type of data must capture the duality between temporal/structural and feature-based information. Yet state-of-the-art deep learning models often struggle to learn especially short-term behavioral interaction signals, such as sender intensity or interaction inertia, directly from raw event streams. To address this gap, we propose a statistical feature augmentation method that explicitly encodes behavioral interaction statistics into the input feature space. We evaluate our proposed method on an anomaly detection task across three real-world datasets (Reddit, Wikipedia, MOOC) and seven models spanning both continuous-time and discrete-time architectures. As a baseline, we apply the same models trained on the original embeddings. Our results show, that augmentation consistently improves detection performance. Beyond performance, the enriched input enables fine-grained post-hoc analysis of behavioral importance, since each statistic occupies a dedicated input dimension. In particular, this work showcases a promising approach for merging classical network analysis with deep learning.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.02965",
  "author_total": 3,
  "authors": [
    {
      "name": "Philipp Schlinge"
    },
    {
      "name": "Jean-Luc Schnipper"
    },
    {
      "name": "Martin Atzmueller"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8wj16dj4tvrgw.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.02965",
  "fulltext_url": null,
  "id": "8wj16dj4tvrgw",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.02965",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "cs.SI"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "1702|Advanced Graph Neural Networks"
    ]
  ],
  "openalex": "W7208770034",
  "pdf_url": "https://arxiv.org/pdf/2609.02965",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8wj16dj4tvrgw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:42Z",
      "native_id": "2609.02965",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.02965"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Statistical Feature Augmentation for Anomaly Detection in Dynamic Graphs",
  "title_text": "Statistical Feature Augmentation for Anomaly Detection in Dynamic Graphs",
  "type": "preprint",
  "updated_at": "2026-10-30T12:18:42Z",
  "url": "https://research.draconitic.com/paper/8wj16dj4tvrgw",
  "venue": "arXiv",
  "versions": []
}