Covers graph processing with a focus on Oracle Labs PGX, discussing graph analytics, databases, algorithms, and distributed analytics challenges.
Explores learning from interconnected data with graphs, covering modern ML research goals, pioneering methods, interdisciplinary applications, and democratization of graph ML.
Delves into graph-enhanced machine learning, focusing on fraud detection, malware detection, and recommendation systems.
Explores handling network data, including types of graphs, real-world network properties, and node importance measurement.
Introduces the basics of graph algorithms, covering traversal, representation, and data structures for BFS and DFS.
