@inproceedings{nishide-miwa-2026-mitigating,
    title = "Mitigating Degree Bias in Hypergraphs via Attribute-as-Structure Approach",
    author = "Nishide, Ryusei  and
      Miwa, Makoto",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.81/",
    pages = "1784--1801",
    ISBN = "979-8-89176-380-7",
    abstract = "Entity representation learning on hypergraphs is hindered by degree bias, where nodes with sparse connections suffer from limited structural information for aggregation. Prevailing ``attribute-as-feature{``} approaches, which treat rich textual attributes (e.g., titles, abstracts, keywords) merely as node features, fail to address this structurally rooted problem as they do not create new aggregation pathways. To overcome this limitation, we propose a novel ``attribute-as-structure{``} approach specifically designed for heterogeneous hypergraphs. Our approach integrates attributes directly into the hypergraph topology as distinct node types, creating new structural pathways to enrich sparsely connected entities while preserving semantic distinctiveness within complex many-to-many hyperedge interactions. We introduce an entity-attribute aware learning framework featuring two key innovations: (1) a specialized heterogeneous hypergraph encoder with dual attention mechanisms{---}self-attention for entity-entity relationships and cross-type attention for entity-attribute relevance, and (2) Attribute-Attentive Contrastive Learning (AACL), a novel objective that dynamically weighs attribute importance while explicitly aligning entity representations with their structural attributes. Experiments on multiple hypergraph datasets demonstrate consistent improvements in node classification performance, with particularly significant gains for structurally sparse nodes, demonstrating the effectiveness of our approach for degree bias mitigation."
}