Towards Neural Scaling Laws for Foundation Models on Temporal Graphs

Datasets provided in this storage are introduced in the paper: Towards Neural Scaling Laws for Foundation Models on Temporal Graphs Each .csv file represents all transactions of the token network that has the same name as the file name (<tokenname.csv>) Each transaction corresponds to a row in each file. Each transaction has: blockNumber : is the block ID of Ethereum that includes this transaction timestamp: time that the transaction is made in UNIX timestamp format tokenAddress : the address that specifies a unique ERC20 token from: address of sender to: address of receiver value: the amount the transaction fileBlock: we split the whole number of blocks count to 35 buckets and assigned the bucket ID to the transaction to trace the blocks Raw .csv will be used to divide into generate edgeslist and label, which indicates all node interactions and labels for each snapshot respectively, with help of functions from TGS_Handler defined in TGS.py inside the TGS package (see the code in the Github repository provided along with this data storage)本存储库所提供的数据集已在论文《面向时序图基础模型的神经缩放定律》（Towards Neural Scaling Laws for Foundation Models on Temporal Graphs）中予以介绍。 每个.csv文件均对应与文件名同名的代币网络的全部交易（格式为<tokenname.csv>）。 每笔交易在文件中以一行数据呈现。 每笔交易包含以下字段： - blockNumber：包含该交易的以太坊区块ID - timestamp：以Unix时间戳格式记录的交易发生时间 - tokenAddress：用于唯一标识某一ERC20代币的合约地址 - from：交易发起方地址 - to：交易接收方地址 - value：本次交易的代币数量 - fileBlock：我们将总区块数划分为35个分桶，并为每笔交易分配分桶ID，以实现区块追踪。 原始CSV文件将借助TGS包内TGS.py中定义的TGS_Handler工具函数，被处理为边列表（edgeslist）与标签集（label），二者分别对应各快照的全部节点交互信息与对应标签。相关代码可参见随本数据集存储库一同提供的GitHub仓库。