Explore graph-based modeling techniques in computational pathology through this comprehensive lecture by Stanford University PhD candidate Siyi Tang. Delve into the emerging field of leveraging cellular interactions and spatial structures in whole slide images using graph neural networks. Learn about spectral and spatial networks, cell graph construction, adaptive glossage, node embedding, and graph clustering. Examine experiments in cluster assignments, cancer grading, and nuclear sampling. Analyze drawbacks, post-hoc graph expanders, and evaluation metrics like separability scores. Gain insights into qualitative and quantitative assessments, as well as personal takeaways on domain expertise and explanation methods. Engage with cutting-edge research aimed at developing better medical machine learning models and enabling novel scientific discoveries in pathology.