Lectures related to Adjacency list

Graphical Models: Representing Probabilistic Distributions

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

Information Retrieval: Indexing and Retrieval

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

Networks: Computer Representation

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

Graphs: BFS

Introduces elementary graph algorithms, focusing on Breadth-First Search and Depth-First Search.

Graph Algorithms: Modeling and Representation

Covers the basics of graph algorithms, focusing on modeling and representation of graphs in memory.

Handling Network Data

Covers handling network data, types of graphs, centrality measures, and properties of real-world networks.

Graphs in Deep Learning: Applications and Techniques

Explores the role of graphs in deep learning, focusing on their structure, applications, and techniques for processing graph data.

Graphs: Properties and Representations

Covers graph properties, representations, and traversal algorithms using BFS and DFS.

Graph Algorithms: Memory Management and Traversal

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

Depth-First Search: Traversing and Sorting Graphs

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

Spatial relations and topology: summary

Covers spatial relations in general, focusing on topology and its role.

Link-based Ranking: Graph Mining

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

Networked Control Systems: Properties and Connectivity

Explores properties of matrices, irreducibility, and graph connectivity in networked control systems.

Link Indexing

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

Consensus Theorem for Communication Networks

Explores the consensus theorem for communication networks and the implications of various consensus properties in networked control systems.

Graph Representation and Traversal

Introduces graph theory basics, graph representation methods, and traversal algorithms like BFS and DFS.

Optimal Binary Search Tree

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

Irreducible Matrices and Strong Connectivity

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

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Consensus in Networked Control Systems

Explores consensus in networked control systems through graph weight design and matrix properties.