This topic is about advancing AI-based recommendation methods through the integration of large language models and graph message passing networks. The project aims to revolutionize how we predict and understand linkages within academic citation networks.

What are the tasks?

• Implementing and testing algorithms for link prediction, community detection, node classification, and potentially other graph-supervised learning tasks.
• Exploring the trade-offs between the utilization of textual and structural features in link prediction algorithms, and devising methods to efficiently combine these features.

What prerequisites do you need?

• A strong interest in machine learning, natural language processing, or graph theory.
• Proficiency in programming, preferably in Python, with experience in PyTorch or TensorFlow.
• Eagerness to engage with state-of-the-art research in link prediction and text mining.