{
  "abstract": "Temporal heterogeneous graphs offer a natural abstraction for dynamic relational systems in which diverse node and relation types co-exist and evolve over time. Learning on such graphs requires jointly modeling cross-type structural heterogeneity and the temporal dynamics of interactions, yet existing methods still struggle to reconcile parameter-efficient cross-type transfer with relation-aware specialization, and typically inject time only as additive features outside the attention kernel. We propose \\textbf{THGFM}, a web-scale temporal heterogeneous graph fusion model that addresses both limitations within a unified dual-path architecture. THGFM couples a \\textit{Shared-Space Temporal Attention} branch for parameter-efficient cross-type transfer with a \\textit{Relational Type-Partitioned Temporal Attention} branch for relation-aware specialization, and integrates them through \\textit{Dual-Path Relational--Shared Fusion}, instantiated with \\textit{Type-Conditioned Non-Competitive Gated Sum Fusion}: a adaptive mechanism that assigns independent, type-conditioned feature-wise gates to the shared and specialized branches, allowing both to be amplified or suppressed without zero-sum competition. To directly incorporate relative time into the attention score, THGFM further introduces \\textit{Rotary Temporal Attention}, which rotates queries and keys by half-phases of relative time before matching. THGFM consistently outperforms baseline graph transformer models on academic graphs benchmarks, delivering a $+3.25\\%$ six-task mean gain, with peak relative gains of $+12.37\\%$ on OAG-CS PV, $+4.87\\%$ on PF-$L_2$, and $+1.18\\%$ on PF-$L_1$, and $+4.24\\%$, $+3.73\\%$, and $+4.61\\%$ on OGBN-MAG, HTAG-ArXiv, and HTAG-DBLP, respectively.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2607.27303",
  "author_total": 4,
  "authors": [
    {
      "name": "Yixin Peng"
    },
    {
      "name": "Diego Collarana"
    },
    {
      "name": "Er Jin"
    },
    {
      "name": "Stefan Decker"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/emg3hvnhx6h06.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2607.27303",
  "fulltext_url": null,
  "id": "emg3hvnhx6h06",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2607.27303",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.CL"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.ML"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2607.27303",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/emg3hvnhx6h06.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:26Z",
      "native_id": "2607.27303",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2607.27303"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "THGFM: Dual-Branch Temporal Heterogeneous Graph Fusion Model",
  "title_text": "THGFM: Dual-Branch Temporal Heterogeneous Graph Fusion Model",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:26Z",
  "url": "https://research.draconitic.com/paper/emg3hvnhx6h06",
  "venue": "arXiv",
  "versions": []
}