cash-app-inc/open-sea-rlr

--- fontsize: 10pt geometry: margin=0cm output: pdf_document license: cc-by-4.0 --- # Open Sea for Recent Link Regression ## Summary 1. **Name:** `open-sea-rlr` 2. **Description:** This dataset involves a Non-Fungible Token (NFT) transaction network between the users of [Open Sea](https://opensea.io/). Vertices represent unique sellers and buyers of NFTs. Each transaction is characterized by a timestamp and a feature vector. Features include binary representations of categorical variables, cryptocurrency exchange rates, and monetary values. The target value for each transaction is computed as the rate of return on investment, normalized by the original purchase price. This metric reflects the profitability of each transaction. 3. **Task:** The task is predicting the rate of return for an investment (NFT purchase). 4. **Date of Creation:** 01.07.2024 5. **Last Update:** 01.07.2024 6. **Original Sources:** https://huggingface.co/datasets/MLNTeam-Unical/NFT-70M_transactions 7. **Contact Information:** [email]([e-mail]) 8. **License:** CC BY 4.0 ## Statistics | Category | Data | |-------------------------|------------| | Number of Nodes | 2,601,107 | | Number of Edges | 25,876,356 | | Number of Node Features | 0 | | Number of Edge Features | 86 | | Number of Timestamps | 7,361,184 | ## Download 1. **Format:** Compressed `data.pt` which involves a python dictionary as follows: ```python data = { "node_attr": None, "edge_index": torch.LongTensor, "edge_time": torch.FloatTensor, "edge_attr": torch.FloatTensor, "edge_label": torch.FloatTensor, "num_nodes": int } ``` 2. **Size:** 322 MB 3. **Location:** https://huggingface.co/datasets/ca-aird/airtraffic2015/blob/main/data.zip ## Citation ``` @article{, title={Benchmarking Edge Regression on Temporal Networks}, author={Muberra Ozmen and Florence Regol and Thomas Markovich}, journal={X}, volume={X}, number={X}, pages={X}, year={X}, publisher={X} } ``` ## Preprocessing Transaction records involve following fields: | Field | Description | Usage | |------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------| | `seller_account` | Address of the NFT seller. | Used as source node | | `winner_account` | Address of the NFT buyer. | Used as destination node | | `tx_timestamp` | Timestamp of the transaction. | Used as edge time | | `token` | Token type used to pay the transaction: converted to categorical data with 0/1 indicator. | Used as edge feature | | `chain` | Blockchain where the transaction occurs: converted to categorical data with 0/1 indicator. | Used as edge feature | | `token_type` | Schema of the token, i.e., ERC721 or ERC1155: converted to categorical data with 0/1 indicator. | Used as edge feature | | `asset_contract_type` | Asset typology, i.e., non-fungible or semi-fungible: converted to categorical data with 0/1 indicator. | Used as edge feature | | `asset_type` | Whether the asset was involved in a simple or bundle transaction: converted to categorical data with 0/1 indicator. | Used as edge feature | | `to_eth` | Conversion rate to convert tokens into Ethereum at the current timestamp: normalized to [0, 1]. | Used as edge feature | | `to_usd` | Conversion rate to convert tokens into US dollars (USD) at the current timestamp: normalized to [0, 1]. | Used as edge feature | | `created_date` | Date of creation of the contract: converted to timestamp. | Used as edge feature | | `token_id` | ID of the NFT. | | | `collection_name` | ID for accessing the collection name: `token_id` is unique within the same collection, so these two are used to identify unique item identification `item_id`. | | | `usd_price` | Price of the transaction expressed in US dollars (USD): used to calculate edge target such that (`usd_price` - NEXT[`usd_gain`]) / `usd_price`. | Used to calculate edge target | | `usd_gain` | Difference between the price and the fees expressed in US dollars (USD): used to calculate edge target such that (`usd_price` - NEXT[`usd_gain`]) / `usd_price` | Used to calculate edge target | ## Notes 1. **Acknowledgements:** - All funding was provided by [Block Inc](https://block.xyz). 2. **References:** - La Cava, L., Costa, D., & Tagarelli, A. (2023). SONAR: Web-based Tool for Multimodal Exploration of Non-Fungible Token Inspiration Networks. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '23), 3200–3204. New York, NY, USA: Association for Computing Machinery. - La Cava, L., Costa, D., & Tagarelli, A. (2023). Visually Wired NFTs: Exploring the Role of Inspiration in Non-Fungible Tokens. arXiv preprint arXiv:2303.17031. - Costa, D., La Cava, L., & Tagarelli, A. (2023). Show me your NFT and I tell you how it will perform: Multimodal representation learning for NFT selling price prediction. In Proceedings of the ACM Web Conference 2023 (WWW '23), 1875–1885. New York, NY, USA: Association for Computing Machinery. ## Author Statement I, Muberra Ozmen, declare that I bear full responsibility for the dataset described herein, including its contents and compliance with applicable laws and regulations. By providing access to this dataset, I confirm that it is released under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). Users are free to: - **Share** — copy and redistribute the material in any medium or format - **Adapt** — remix, transform, and build upon the material for any purpose, even commercially Under the following conditions: - **Attribution** — appropriate credit must be given to the associated publication indicating if any changes were made. This information should be provided in a manner that is reasonable given the medium, means, and context in which the dataset is shared. For any use or redistribution of the dataset not permitted under this license, explicit permission from the dataset's creator is required. The dataset will be hosted on a secure platform that ensures continuous access to the data. We have chosen [Hugging Face](https://huggingface.co/) for its robust infrastructure and capability to handle large datasets. Access to the dataset will be facilitated through a curated interface, providing users with efficient search and retrieval functionalities. **Licensing:** The dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing users to freely use, share, and adapt the material, provided appropriate credit is given to the dataset's creator. **Maintenance:** Regular maintenance of the dataset and its hosting platform will be conducted to ensure data integrity, security, and accessibility. Updates to the dataset, if any, will be promptly integrated into the platform to reflect the most current information available.文档设置： - 字体大小：10pt - 页边距：0cm - 输出格式：pdf_document - 许可证：CC BY 4.0 # 面向近期链接回归的OpenSea（Open Sea）数据集 ## 摘要 1. **名称：** `open-sea-rlr` 2. **描述：** 本数据集围绕OpenSea平台用户间的非同质化代币（Non-Fungible Token, NFT）交易网络构建。图中顶点代表NFT的独立卖家与买家，每一笔交易均由时间戳与特征向量表征。特征包含分类变量的二进制编码、加密货币汇率及货币金额。每笔交易的目标值为以初始购买价格归一化的投资回报率，该指标可反映单笔交易的盈利性。 3. **任务：** 本任务为预测NFT投资的回报率。 4. **创建日期：** 2024年7月1日 5. **最后更新：** 2024年7月1日 6. **原始来源：** https://huggingface.co/datasets/MLNTeam-Unical/NFT-70M_transactions 7. **联系方式：** [邮箱]([e-mail]) 8. **许可证：** CC BY 4.0 ## 统计信息 | 分类项 | 数据量 | |-------------------------|------------| | 节点总数 | 2,601,107 | | 边总数 | 25,876,356 | | 节点特征数 | 0 | | 边特征数 | 86 | | 时间戳总数 | 7,361,184 | ## 下载方式 1. **格式：** 压缩包`data.pt`，内含如下Python字典结构： python data = { "node_attr": None, "edge_index": torch.LongTensor, "edge_time": torch.FloatTensor, "edge_attr": torch.FloatTensor, "edge_label": torch.FloatTensor, "num_nodes": int } 2. **文件大小：** 322 MB 3. **下载地址：** https://huggingface.co/datasets/ca-aird/airtraffic2015/blob/main/data.zip ## 引用格式 @article{, title={Benchmarking Edge Regression on Temporal Networks}, author={Muberra Ozmen and Florence Regol and Thomas Markovich}, journal={X}, volume={X}, number={X}, pages={X}, year={X}, publisher={X} } （注：原文引用项未补充完整，上述为标准BibTeX引用格式模板） ## 预处理说明 交易记录包含以下字段： | 字段名 | 描述 | 用途 | |------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------| | `seller_account` | NFT卖家的钱包地址。 | 用作边的源节点 | | `winner_account` | NFT买家的钱包地址。 | 用作边的目标节点 | | `tx_timestamp` | 交易发生的时间戳。 | 用作边的时间属性 | | `token` | 用于支付交易的代币类型：转换为0/1编码的分类特征。 | 用作边特征 | | `chain` | 交易所在的区块链网络：转换为0/1编码的分类特征。 | 用作边特征 | | `token_type` | 代币标准类型，即ERC721或ERC1155：转换为0/1编码的分类特征。 | 用作边特征 | | `asset_contract_type` | 资产合约类型，即非同质化或半同质化资产：转换为0/1编码的分类特征。 | 用作边特征 | | `asset_type` | 资产交易类型，即单笔或批量交易：转换为0/1编码的分类特征。 | 用作边特征 | | `to_eth` | 当前时间戳下代币兑换为以太坊（Ethereum）的汇率：归一化至[0, 1]区间。 | 用作边特征 | | `to_usd` | 当前时间戳下代币兑换为美元（USD）的汇率：归一化至[0, 1]区间。 | 用作边特征 | | `created_date` | 智能合约的创建日期：转换为时间戳格式。 | 用作边特征 | | `token_id` | NFT的唯一标识ID。 | 无直接用途 | | `collection_name` | 合集名称访问标识符：`token_id`在同一合集内唯一，因此二者可共同作为唯一项目标识`item_id`。 | 无直接用途 | | `usd_price` | 以美元计价的交易成交价：用于计算边目标值，计算公式为(`usd_price` - NEXT[`usd_gain`]) / `usd_price`。 | 用于计算边目标标签 | | `usd_gain` | 交易成交价与手续费的美元差额：用于计算边目标值，计算公式为(`usd_price` - NEXT[`usd_gain`]) / `usd_price` | 用于计算边目标标签 | ## 备注 1. **致谢：** - 本项目所有资助均由Block Inc（https://block.xyz）提供。 2. **参考文献：** - La Cava, L., Costa, D., & Tagarelli, A. (2023). SONAR: 用于非同质化代币灵感网络多模态探索的Web工具。发表于第46届国际ACM SIGIR信息检索研究与发展会议（SIGIR '23）论文集，3200–3204。美国纽约：美国计算机协会。 - La Cava, L., Costa, D., & Tagarelli, A. (2023). 可视化互联NFT：探索灵感在非同质化代币中的作用。arXiv预印本arXiv:2303.17031。 - Costa, D., La Cava, L., & Tagarelli, A. (2023). 展示你的NFT，我将告诉你其表现：用于NFT售价预测的多模态表征学习。发表于2023年ACM Web会议（WWW '23）论文集，1875–1885。美国纽约：美国计算机协会。 ## 作者声明 本人Muberra Ozmen声明，对本数据集承担全部责任，包括其内容与合规性。本数据集以知识共享署名4.0国际许可协议（CC BY 4.0）发布，用户可自由进行以下操作： - **共享** — 在任何媒介或格式中复制和重新分发本材料 - **改编** — 为任何目的（包括商业用途）重新混合、转换和基于本材料创作 需遵守以下条件： - **署名** — 需对关联出版物给予适当署名，并注明是否进行了修改。该信息应以与数据集共享的媒介、方式和背景相适应的方式提供。 未经本许可允许的数据集使用或再分发，需获得数据集创建者的明确书面许可。 本数据集将托管于可确保持续访问的安全平台。我们选择Hugging Face（https://huggingface.co/）因其具备强大的基础设施与大型数据集处理能力。数据集的访问将通过精选界面实现，为用户提供高效的搜索和检索功能。 **许可协议：** 本数据集采用知识共享署名4.0国际许可协议（CC BY 4.0）授权，用户可自由使用、共享和改编本材料，只需对数据集创建者给予适当署名。 **维护机制：** 将定期对数据集及其托管平台进行维护，以确保数据完整性、安全性与可访问性。如有数据集更新，将及时集成至平台以反映最新可用信息。