Lectures related to Graph (abstract data type)

Information Retrieval: Indexing and Retrieval

Covers indexing techniques, distributed retrieval algorithms, and challenges in large-scale web indexing.

Concurrent Programming: Theory to Practice

Explores concurrent programming theory and practice, covering system models, cache coherence, and graph processing.

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

Link-based Ranking: Graph Mining

Explores link-based ranking and graph mining techniques for efficient web graph analysis.

Graph Neural Networks: Interconnected World

Explores learning from interconnected data with graphs, covering modern ML research goals, pioneering methods, interdisciplinary applications, and democratization of graph ML.

Learning from the Interconnected World with Graphs

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

Graph Algorithms: Memory Management and Traversal

Explores memory management, graph representation, and traversal algorithms in Python, emphasizing BFS and DFS.

Graph Algorithms: Modeling and Traversal

Covers graph algorithms, modeling relationships between objects, and traversal techniques like BFS and DFS.

Graphical Models: Representing Probabilistic Distributions

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

Belief Propagation in Random Graphs

Explores belief propagation in random graphs and Bethe free entropy.

Algorithmes: introduction

Covers the basics of algorithms, problem-solving, and efficient resolution methods.

Statistical Analysis of Network Data: Structures and Models

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

Irreducible Matrices and Strong Connectivity

Explores irreducible matrices and strong connectivity in networked control systems, emphasizing the importance of adjacency matrices and graph structures.

Predicting Reaction Yields with Deep Learning

Explores predicting reaction yields with deep learning models and the importance of high-quality data sets in chemistry.

Depth-First Search: Traversing and Sorting Graphs

Explores depth-first search, breadth-first search, graph representation, and topological sorting in graphs.

Ising Simulation: Update Methods and Measurements

Covers the Ising simulation, focusing on update methods and measurements.

Efficient Graph Queries: Memory Constraints

Explores scaling pattern matching in large graphs and optimizing query execution under memory constraints using work-sharing and batch processing.

Optimal Binary Search Tree

Explores optimal binary search trees to minimize expected search cost and discusses graphs representation using adjacency matrices and lists.

Networks: Computer Representation

Covers the concept of adjacency matrix and adjacency lists for representing networks.

Link Indexing

Explores adjacency lists, locality, similarity, and efficient representation in link indexing.