Class schedule

Date Description Slides  HW/Project
Thu. 01/17 Introductions, class organization, networks, context, examples Block 1
Tue. 01/22 Graphs, digraphs, degrees, movement, strong and weak connectivity Block 2a
Thu. 01/24 Families, algebraic graph theory, data structures and algorithms
Tue. 01/29 Travel to Wkshp. on Machine Learning for Network Data - No class
Thu. 01/31 Inference, models, point and set estimates, hypothesis testing Block 2b
Fri. 02/01 Tutorials on inference about a mean and linear regression    
Tue. 02/05 Graph visualization, stages of network mapping, mapping Science Block 3a
Thu. 02/07 Large graph visualization, k-core decomposition, Internet mapping    
Tue. 02/12 Travel to Graph Signal Processing Wkshp. - No class    
Thu. 02/14 Travel to Graph Signal Processing Wkshp. - No class    
Tue. 02/19 Degree distributions, Erdos-Renyi random graphs and power laws Block 3b HW1 due
Thu. 02/21 Visualizing and fitting power laws, preferential attachment    
Fri. 02/22 Closeness, betweeness and eigenvector centrality measures Block 3c
Tue. 02/26 Web search, hubs and authorities, Markov chains review
Thu. 02/28 PageRank, fluid and graph random walk models, distributed algorithms    
Fri. 03/01 Cohesive subgroups, clustering, connectivity, assortativity mixing Block 3d
Tue. 03/05 Strength of weak ties, community structure in networks Block 4a
Thu. 03/07 Girvan-Newmann method, hierarchical clustering, modularity   Proposal
Tue. 03/12 Spring break - No class    
Thu.  03/14 Spring break - No class    
Tue. 03/19 Modularity optimization, graph cuts, spectral graph partitioning    
Thu. 03/21 Sampling, Horvitz-Thompson estimation, graph sampling designs Block 4b HW2 due
Tue. 03/26 Network estimation of totals, groups size, degree distributions
Thu. 03/28 Random graph models, model-based estimation, significance, motifs Block 4c
Tue. 04/02 Small-world,  preferential attachment and copying models    
Thu. 04/04 Exponential random graph models, construction and estimation   Prog. Report
Tue. 04/09 Topology inference, link prediction, scoring and classification Block 4d
Thu. 04/11 Inference of association networks, tomographic inference
Tue. 04/16 Nearest-neighbor prediction of processes, Markov random fields Block 5a
Thu. 04/18 Graph kernel-regression, kernel design, protein function prediction   HW3 due
Tue. 04/23 Diseases and the networks that transmit them, epidemic modeling Block 5b
Thu. 04/25 Network flow data, routing and traffic matrices, gravity models Block 5c
Tue. 04/30 Traffic matrix estimation, network flow costs, network kriging
Fri. 5/3 In-class student project presentations Presentation