Link Prediction With node2vec in Physics Collaboration Network

The blog post by Antonio Filipovic provides a comprehensive tutorial on generating link predictions using the node2vec algorithm within the MAGE framework, applied to the High Energy Physics Collaboration Network dataset. It covers the process of importing and parsing the dataset into Memgraph, splitting the dataset into training and testing sets, and using node2vec to generate node embeddings. Through these embeddings, potential future connections between nodes are predicted using cosine similarity, and these predictions are evaluated using precision@K metrics. The tutorial highlights the importance of hyperparameter tuning in node2vec, explains the theory behind link prediction, and provides practical code snippets for implementation. The post concludes by showcasing the precision of predictions and encouraging engagement with graph analytics and the MAGE platform.