Concept

Centrality

Related lectures (32)

Explores centrality, hubs, eigenvectors, clustering coefficients, small-world networks, network failures, and percolation theory in brain networks.

Centrality and Hubs

Delves into centrality and hubs in network neuroscience, exploring node importance, small-world networks, brain structural connectome, and percolation theory.

Handling Networks: Graph Theory

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

Handling Networks: Graph Theory

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

Handling Network Data

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

Handling Network Data

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

Social and Information Networks: Ranking

Explores the significance of ranking in networks, emphasizing algorithms like PageRank and HITS for web page ranking.

Statistical analysis of network data

Covers stochastic properties, network structures, models, statistics, centrality measures, and sampling methods in network data analysis.

Graph Statistics: Random Graphs, Graph Homomorphisms, and Network Analysis

Explores graph statistics, random graph generation, network analysis, centrality measures, and clustering coefficients.

Node Degree and Strength

Explores node degree and strength in network neuroscience, discussing random vs real networks and the challenges of fitting power laws to real data.

Network Analysis: Methods and Applications

Explores network analysis methods, operationalizing concepts, historical applications, and challenges in treating time within networks.

Social Network Analysis: Modularity Measure

Explores the computation of the modularity measure and betweenness centrality in graphs for community detection.

Nonparametric Network Summaries

Covers nonparametric network summaries, centrality measures, network modularity, and clustering coefficients.

Statistical Analysis of Network Data: Structures and Models

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

Fixed Points in Graph Theory

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

Scale-Free Networks: Power Laws and Preferential Attachment

Explores scale-free networks, power laws, preferential attachment, and network assortativity.

Network Formation: Random Connection Models

Explores network formation through random connection models and node degree distribution.

Stochastic Blockmodel Estimation

Explores Stochastic Blockmodel estimation, spectral clustering, network modularity, Laplacian matrix, and k-means clustering.

Consensus Algorithms: Weight Assignment and Applications

Explores the design of graph weights for consensus and applications in sensor networks.

Link-based Ranking: Fundamentals and Algorithms

Covers the fundamentals and algorithms of link-based ranking, including anchor text indexing, PageRank, HITS, and practical implementations.