Graph Neural Networks
Scalability is a significant challenge for graph neural networks, especially when handling large datasets with numerous vertices. The discussion highlights the complexities of labeling malicious domains within a graph, emphasizing the positive unlabeled problem in machine learning. The importance of learning behavioral patterns rather than specific domain characteristics is underscored, as malicious domains can quickly change. Ultimately, the model functions as a binary classifier, providing confidence scores for its predictions.In this clip
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