The evaluation metric for this competition is
[Pearson Correlation Coefficient](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient)
(PCC). PCC is a measure of linear correlation between two sets of data. It is the ratio between the covariance of two variables and the product of their standard deviations, and is given by:
PCC(X,Y) = \frac{cov(X,Y)}{\sigma_X \sigma_Y}
where cov(X,Y) denotes the covariance of X and Y, \sigma_X, \sigma_Y is the standard deviation of X and Y, respectively.
Before the submission deadline , you can only view the PCC evaluated on 50% of the dataset on the leaderboard. After the deadline, we will reveal your model PCC evaluated on the entire dataset to rate your performance.
Submission Format
Your submission file should contain two columns: id and pred_label. Each test author node  is allocated a unique id, and you need to use this id as your prediction index. For example, the first element in the first row
0
means a test node with author id 0. If you predict that it is a key author in the network, the value of the corresponding row of your uploaded file is 0,1.
The file should contain a header and have the following format:
id,pred_label
0,1
1,0
17,0
Detailed description can be found in ‘data’ page.