Graph Neural Networks: Interconnected WorldExplores learning from interconnected data with graphs, covering modern ML research goals, pioneering methods, interdisciplinary applications, and democratization of graph ML.
Causal Inference & Directed GraphsExplores causal inference, directed graphs, and fairness in algorithms, emphasizing conditional independence and the implications of DAGs.
Graph Algorithms: BasicsIntroduces the basics of graph algorithms, covering traversal, representation, and data structures for BFS and DFS.
Handling Network DataExplores handling network data, including types of graphs, real-world network properties, and node importance measurement.
Graph Processing: Oracle Labs PGXCovers graph processing with a focus on Oracle Labs PGX, discussing graph analytics, databases, algorithms, and distributed analytics challenges.
DFS Continuation: Topological SortCovers topics like DFS output, edge classification, acyclic graphs, correctness, time analysis, SCCs, and the Topological Sort algorithm.