Graph Pattern Matching: Work-sharing

This lecture covers the optimization of graph pattern matching through work-sharing techniques, including transitioning from sequential to parallel matching, addressing load imbalance, and utilizing work-stealing strategies. It also discusses experimental setups, such as the Social Network Benchmark, and emphasizes the importance of context-aware parallelization and node dependencies in pattern mining at scale.