{
  "abstract": "Context-aware international event forecasting must preserve context-specific evidence while drawing on related interactions across semantic boundaries. We propose the hierarchical adaptive fusion and separation network (HA-AFSNet), which learns this balance before temporal encoding. A history-conditioned coefficient jointly fuses entity and relation states, linking information allocation to the participant-ranking objective. The evaluated task ranks candidate participants conditional on semantic context labels derived from articles associated with the queried events. Across five seeds on three GDELT regional graphs, HA-AFSNet improves mean reciprocal rank (MRR) over SeCoGD by 9.73–11.35%. Compared with a learned static gate, the mean MRR gains are 0.0109–0.0149, with positive pointwise 95% paired Student intervals under the stated distributional assumptions. With only five seed pairs, this statistical evidence is limited and conditional on the evaluated splits. The conditioning branch accounts for 1.68–1.78% of the full model’s parameters. Complete test-period coefficient trajectories show how the allocation varies across dates and receiving contexts. These results support history-conditioned information allocation for participant ranking within the supplied event-associated context labels.",
  "abstract_shown": true,
  "affiliations": [
    "School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China",
    "China People’s Police University, Langfang 065000, China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Yunbo Zhang",
      "orcid": "0009-0008-3746-3785"
    },
    {
      "affs": [
        0
      ],
      "name": "Jun Wu"
    },
    {
      "affs": [
        1
      ],
      "name": "Yu Wang"
    },
    {
      "affs": [
        1
      ],
      "name": "Jiayi Song"
    },
    {
      "affs": [
        1
      ],
      "name": "Jing Liu"
    },
    {
      "affs": [
        1
      ],
      "name": "Jicheng Niu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dxxv031gg7ank.bib",
  "cited_by": 0,
  "doi": "10.3390/math14213876",
  "fulltext_url": null,
  "id": "dxxv031gg7ank",
  "issns": [
    "2227-7390"
  ],
  "landing_url": "https://www.mdpi.com/2227-7390/14/21/3876",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1702|Topic Modeling"
    ]
  ],
  "openalex": "W7223667603",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/dxxv031gg7ank.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:29Z",
      "native_id": "10.3390/math14213876",
      "source": "crossref",
      "url": "https://www.mdpi.com/2227-7390/14/21/3876"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2227-7390",
      "subfield": "general"
    }
  ],
  "title": "Adaptive Information Coordination for Context-Aware International Event Forecasting with Temporal Knowledge Graphs",
  "title_text": "Adaptive Information Coordination for Context-Aware International Event Forecasting with Temporal Knowledge Graphs",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:18:29Z",
  "url": "https://research.draconitic.com/paper/dxxv031gg7ank",
  "venue": "Mathematics",
  "versions": []
}