Practical Dynamic Graph Algorithms - Data Structures and Connections Between Models

Explore dynamic graph algorithms and their practical applications across multiple computational models in this 43-minute lecture by Quanquan Liu from Northwestern University. Delve into efficient data structures and techniques for solving dynamic graph problems in shared-memory work-depth, MPC, and differential privacy models. Examine specific data structures used for k-core decomposition, densest subgraph, triangle counting, and other local graph problems. Gain insights into the characteristics that make these structures efficient across various computational paradigms, enhancing your understanding of dynamic algorithms in practical settings.