elliptic_construction.md

Instructions for processing the Elliptic data set

The Elliptic data set, downloadable from https://www.kaggle.com/ellipticco/elliptic-data-set, consists of a dynamic graph that can be used for node classification. It was used, for example, for experimentation by the EvolveGCN paper https://arxiv.org/abs/1902.10191. A preprocessed version of the data set can be read by the EvolveGCN code, specifically, the dataloader elliptic_temporal_dl.py in https://github.com/IBM/EvolveGCN. Here are the preprocessing instructions.

Step 0: Download data

Download data from https://www.kaggle.com/ellipticco/elliptic-data-set. You will see three files elliptic_txs_features.csv, elliptic_txs_classes.csv, and elliptic_txs_edgelist.csv.

Step 1: Create a file named elliptic_txs_orig2contiguos.csv and modify elliptic_txs_features.csv.

The file elliptic_txs_features.csv contains node features, one node each line. Each line contains 167 numbers, where the first number denotes node id. For example, the first three numbers in the first line are

230425980,1,-0.1714692896288031

Here, 230425980 is the node id. You will replace this id by the line number (starting from 0). Moreover, make the first number and second number floating point numbers. That is, in the modified elliptic_txs_features.csv, the three numbers in the first line should be

0.0,1.0,-0.1714692896288031

In the newly created elliptic_txs_orig2contiguos.csv, the first line is the header

originalId,contiguosId

and the lines that follow contain the id conversion information. For example, the line after the header line should be

230425980,0

Step 2: Modify elliptic_txs_classes.csv

The file elliptic_txs_classes.csv contains node labels. Because we have converted the node ids, we need to modify this file accordingly. We also use numeric values to denote the labels.

Specifically, the classes unknown, 1, and 2 are changed to -1.0, 1.0, and 0, respectively. For example, the line after the header line is changed from

230425980,unknown

to

0.0,-1.0

Additionally, the header line is never changed. It is always

txId,class

Step 3: Create a file named elliptic_txs_nodetime.csv

This file will add a time stamp to each node.

The header line is

txId,timestep

Each line afterward will contain two numbers. The first number is the new node id and the second number is the time stamp.

The time stamp appears in the second column of elliptic_txs_features.csv. Recall that the first three numbers in the first line of the original elliptic_txs_features.csv are

230425980,1,-0.1714692896288031

The second number 1 indicates time stamp. We will use a zero based indexing and hence shift this number down by 1.

Therefore, the line after the header line of the new file elliptic_txs_nodetime.csv is

0,0

indicating that the time stamp of node 0 is 0.

Step 4: Modify elliptic_txs_edgelist.csv and rename it to elliptic_txs_edgelist_timed.csv

The header line is changed from

txId1,txId2

to

txId1,txId2,timestep

For each line that follows, the two numbers indicating old node ids are changed to new node ids, followed by time stamp (as floating point number). These two nodes always have the same time stamp in elliptic_txs_nodetime.csv (I recommend doing a sanity check by yourself). Then the edge with these two nodes has a time stamp the same as the node time stamps.

Therefore, the line after the header line in elliptic_txs_edgelist.csv:

230425980,5530458

will be changed to, in elliptic_txs_edgelist_timed.csv:

0,1,0.0

because the new node id for 230425980 is 0 and that for 5530458 is 1. The time stamp for these two nodes are 0.

With all above preprocessing steps done, you should be able to use the dataloader elliptic_temporal_dl.py to load in the data set.