Lectures related to Networks: Paths

Dynamic Programming: Shortest Paths Algorithms

Explores dynamic programming strategies for finding shortest paths in networks with various algorithms and complexities.

Introduction to Networks and Brain Networks

Introduces the fundamentals of network science, focusing on brain networks and their historical breakthroughs.

Networks, Flows MOOC: Optimization: principles and algorithms - Network and discrete optimization

Covers the definition of flow in a network and flow calculation.

Topology in Complex Networks: Insights from Topological Data Analysis

Explores the role of higher-order topological properties in complex networks using topological data analysis for structural break and price anomaly detection.

Networks: Paths and Components MOOC: Optimization: principles and algorithms - Network and discrete optimization

Explores simple paths, connectivity, equivalence classes, and connected components in directed graphs.

Non-parametric regression for networks

Explores non-parametric regression for networks, covering object data analysis, network graphs, extrinsic distances, and practical projections.

Graph Theory: Connectivity and Properties

Explores the properties of undirected and directed graphs, emphasizing connectivity and network topology modeling.

Dynamic Programming: Pascal's Triangle & Floyd's Algorithm

Explores dynamic programming through Pascal's Triangle and Floyd's Algorithm.

Networks: Structure and Properties

Explores the structure and properties of networks, including dating and protein networks, small-world effect, hubs, and scale-free property.

Connectivity in Graph Theory

Covers the fundamentals of connectivity in graph theory, including paths, cycles, and spanning trees.

Graph Algorithms: Ford-Fulkerson and Strongly Connected Components

Discusses the Ford-Fulkerson method and strongly connected components in graph algorithms.

Networks: Trees MOOC: Optimization: principles and algorithms - Network and discrete optimization

Explains the concept of trees in graph theory and the definition of a spanning tree.

Neural Signals and Connectomes

Explores neural signals, connectomes, graph theory, and multi-voxel pattern analysis in fMRI trials.

Matrices and Networks

Explores the application of matrices and eigendecompositions in networks.

Handling Networks: Graph Theory

Covers the fundamentals of handling networks and centrality measures in graph theory.

Handling Networks: Graph Theory

Explores graph theory concepts, centrality measures, and real-world network properties, providing insights into handling diverse types of networks.

Epidemic Spreading Models

Covers classical models of epidemic spreading and dynamics on networks with examples.

Graph Theory Fundamentals

Covers the fundamentals of graph theory, including vertices, edges, degrees, walks, connected graphs, cycles, and trees, with a focus on the number of edges in a tree.

Flow Networks: Understanding Flows and Cuts in Algorithms

Covers flow networks, focusing on flows, cuts, and their applications in algorithms.

Handling Network Data

Explores handling network data, including types of graphs, real-world network properties, and node importance measurement.