从投影图重建超图，利用边重数提升准确性
MARIOH: Multiplicity-Aware Hypergraph Reconstruction
- 通过边重数识别确定的二元超边，缩小搜索空间
- 结合结构与重数特征预测超边，最高比现有方法准确率高74.51%
- 不仅关注高置信候选，还探索低置信选项以发现新可能
超图能有效建模传统成对图无法捕捉的高阶交互关系。然而实际限制常导致其被简化为投影图，造成信息丢失和高阶关系表示模糊。本文提出MARIOH，一种监督式方法，通过利用边重数从投影图重构原始超图。为应对巨大搜索空间的挑战，MARIOH融合三项关键设计：(a) 识别可证明的二元超边，减少候选集；(b) 利用结构与重数相关特征预测候选是否为超边；(c) 不仅聚焦高置信候选，也考察低置信项以探索替代可能性。这些机制使MARIOH能高效且有效地探索搜索空间。在10个真实数据集上的实验表明，其重构准确率最高比现有最优方法提升74.51%。
原文摘要 · Abstract (English)
Hypergraphs offer a powerful framework for modeling higher-order interactions that traditional pairwise graphs cannot fully capture. However, practical constraints often lead to their simplification into projected graphs, resulting in substantial information loss and ambiguity in representing higher-order relationships. In this work, we propose MARIOH, a supervised approach for reconstructing the original hypergraph from its projected graph by leveraging edge multiplicity. To overcome the difficulties posed by the large search space, MARIOH integrates several key ideas: (a) identifying provable size-2 hyperedges, which reduces the candidate search space, (b) predicting the likelihood of candidates being hyperedges by utilizing both structural and multiplicity-related features, and (c) not only targeting promising hyperedge candidates but also examining less confident ones to explore alternative possibilities. Together, these ideas enable MARIOH to efficiently and effectively explore the search space. In our experiments using 10 real-world datasets, MARIOH achieves up to 74.51% higher reconstruction accuracy compared to state-of-the-art methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。