Efficient Graph Queries: Memory Constraints

Related lectures (29)

Page 2 of 3

Explores learning from interconnected data using graphs, covering challenges, GNN design, research landscapes, and democratization of Graph ML.

Explores statistical analysis of network data, covering graph structures, models, statistics, and sampling methods.

Explores data wrangling with Hadoop, emphasizing storage formats and Hive for big data processing.

Introduces the relational model, SQL, keys, integrity constraints, ER translation, weak entities, ISA hierarchies, and SQL vs. noSQL.

Focuses on fixed points in graph theory and their implications in algorithms and analysis.

Explores practical aspects of solving parity games, including winning strategies, algorithms, complexity, determinism, and heuristic approaches.

Covers the SQL language for interacting with databases through structured queries, including selecting, filtering, aggregating, and sorting data.

Covers graphical models for probabilistic distributions using graphs, nodes, and edges.

Explores causal inference through learning graph structures for causal reasoning from observational data.

Covers the basics of the relational model in database management systems, including data models, primary and foreign keys.