Academic Graph

Academic graphs represent scholarly publications and their relationships (authors, citations, topics) as interconnected networks, aiming to facilitate knowledge discovery and analysis beyond traditional keyword searches. Current research focuses on developing efficient graph partitioning algorithms, often leveraging pre-trained deep graph learning models and incorporating natural language processing techniques (like transformers and graph neural networks) to analyze textual content and infer relationships. These advancements enable improved question answering systems, more accurate scholar profiling, and enhanced tools for identifying interdisciplinary research areas, ultimately accelerating scientific progress and improving resource allocation.