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Cross-validation was performed to evaluate the performance of our method and for parameter optimization.
In addition to the internal evaluation using link perturbation approach, we evaluate the performance of our method using an external dataset, namely ChEMBL version 15 database.
In this section, we evaluate the performance of our method on several datasets.
We first evaluate the performance of our method on ShakeFive2 with different model configurations.
Finally, we evaluate the performance of our method on both synthetic records and field data.
We use the dataset DUC20013 to evaluate the performance of our method.
Experiments are done on the worldwide datasets to evaluate the performance of our method.
These labels, referred to as "truth" annotation in this paper, are used to evaluate the performance of our method.
To evaluate the performance of our method, we use the public datasets Polo [13, 15] and TUD [32, 33].
Next, we evaluate the performance of our method in the multi-cluster scenarios with K = 4 users.
Here, we evaluate the performance of our method on edges that did not occur in the recent past.
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