Social and Information Networks: Structure

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Related lectures (32)

Page 3 of 4

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

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

Statistical analysis of network data

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

Scale-Free Networks: Power Laws and Preferential Attachment

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

Handling Network Data

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

Statistical Analysis of Network Data: Structures and Models

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

Centrality and Hubs

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

Graph Coloring II

Explores advanced graph coloring concepts, including planted coloring, rigidity threshold, and frozen variables in BP fixed points.

Clustering: Unsupervised Learning

Explores clustering in high-dimensional space, covering methods like hierarchical clustering, K-means, and DBSCAN.

Search and Routing Protocols

Explores unstructured and structured search and routing protocols, emphasizing the importance of network structure assumptions and introducing the 'Bubble Storm' algorithm.

Dynamic Communication Networks: Structures and Predictability

Explores fundamental structures of dynamic communication networks, including predictability in human mobility and network communities.