Hypergraphx-data: 一个更高阶网络数据的存储库
Hypergraphx-data: a repository for higher-order network data
AI总结：
本研究提出Hypergraphx-data，一个用于更高阶网络分析的现实世界超图数据存储库，涵盖社交网络、生物学和金融等领域，支持加权、定向、时间序列和多层超图配置，并提供开放JSON格式和二值化格式以确保数据的可重复性和完整性。
摘要：
网络数据集的可用性通过使经验分析及其可重复性、算法开发、模型验证和基准测试成为可能，推动了网络科学、机器学习及相关领域的研究。现有的存储库，如SNAP和Netzschleuder，已经使传统网络数据集广泛可用，带有元数据、指标和基本可视化。然而，它们主要关注于成对交互，限制了数据访问到具有多体相互作用的系统。为了弥补这一差距，我们创建了Hypergraphx-data，一个包含现实世界超图数据集的存储库，用于更高阶网络分析，涵盖从社交网络到生物学和金融的不同领域，并支持加权、定向、时间序列和多层超图配置。每个数据集都包含关系信息和元数据，以开放JSON格式和二值化格式提供给Hypergraphx。我们提供了一个用户友好的界面，以方便浏览、过滤和访问数据集，同时通过基于哈希的验证和数据版本控制确保完整性和可重复性。该存储库可在https://hgx-team.github.io/hypergraphx-data访问。
英文摘要：
The availability of network datasets advances research in network science, machine learning and related fields by enabling empirical analyses and their reproducibility, algorithm development, model validation and benchmarking. Existing repositories, such as SNAP and Netzschleuder, have made traditional network datasets widely accessible with metadata, metrics, and basic visualizations. However, they primarily focus on pairwise interactions, limiting data access to systems with many-body interactions. To address this gap, we created hypergraphx-data, a repository of real-world hypergraph datasets for higher-order network analysis, spanning different domains from social networks to biology and finance, and supporting configurations such as weighted, directed, temporal, and multiplex hypergraphs. Each dataset includes relational information and metadata, provided in an open JSON format and a binarized format for Hypergraphx. We provide a user-friendly interface to facilitate browsing, filtering, and accessing the datasets, while also ensuring integrity and reproducibility through hash-based verification and data versioning. The repository is available at https://hgx-team.github.io/hypergraphx-data
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
