Lecture 01 




Introduction and Course Summary 
Lecture 02 




Graph notation and representation 
Lecture 03 




Application: Bayesian Networks (and Complexity) 
Lecture 04 




Graph connectivity and traversal 
Lecture 05 




Nodes, paths and cycles 
Lecture 06 




Application: Genome Reconstruction 
Lecture 07 




Graph features 
Lecture 08 




Graph features (2) 
Lecture 09 




Application: PageRank 
Lecture 10 




Random Graphs: ErdosRenyi random graphs 
Lecture 12 




Random Graphs: spatiallyembedded and smallworld networks 
Lecture 13 




Random Graphs: preferentialattachment models 
Lecture 14 




Random Graphs: HOT and COLD 
Lecture 15 




Modelling with Graphs, and Artificial Neural Networks 
Lecture 16 




Operations on graphs (unary operators) 
Lecture 17 




Operations on graphs (binary operators) 
Lecture 18 




Application: Graph Matching 
Lecture 19 




Shortest paths (FloydWarshall algorithm) 
Lecture 20 




Path algebras 
Lecture 21 




Pathproblem algorithms 
Lecture 22 




Network Topology Measurement 
Lecture 23 




Network Sampling 
Lecture 24 




Network Tomography 
Lecture 25 




Network Topology Inference 
Lecture 26 




Revision 