Citation Graph

Citation graphs represent the relationships between scientific publications, visualized as networks where nodes are papers and edges are citations, aiming to understand knowledge flow and impact. Current research focuses on improving methods for analyzing these graphs, including using graph neural networks (GNNs) to learn paper embeddings and predict citations, optimizing graph structures for improved GNN performance, and leveraging these graphs to enhance tasks like paper recommendation and summarization. This work is significant because it helps researchers navigate the vast and ever-growing body of scientific literature, enabling more efficient knowledge discovery and potentially mitigating biases in citation practices.