Link Prediction on Graphs Using NLP Embedding

This paper summarizes our approach (team Math-Land) to the DSAA 2023 Data Science Competition, which focuses on link prediction. Our proposed model is based on embedding techniques commonly used for natural language processing, and the embedding is constructed as part of the neural network training, eliminating the need for a separate step. We train the model using the binary cross entropy loss and the Adam optimizer. The approach achieves high F1-scores on validation and test sets.