MiNT: Multi-Network Training for Transfer Learning on Temporal Graphs

Datasets provided in this storage are introduced in the paper: MiNT: Multi-Network Training forTransfer Learning 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 To use the same setting as described in the papers, we include edge list and label that contain node interactions and labels for each snapshot in each token network. Each transaction in the edge list also has "from","to" and "amount" fields, but with an additional "snapshot" field to indicate the index of the snapshot that the transaction below to Each row in label file indicates the ground truth label of the snapshot having an index corresponding to the index of the row (e.g first row indicates the label of the first snapshot) We provided the way to generate edge lists and label files in the following Github repository: https://github.com/benjaminnNgo/ScalingTGNs/blob/main/script/utils/TGS.py However, we also provide raw .csv to divide into generate edgeslist and label with a different setting. There are two set of labels for two different tasks, MiNT_labels_Edge_GS is for Edge Growth or Shrink and MiNT_labels_LargeConComp_GS is for Largest Connect Component Growth or Shrink task