Complex-Network Modelling and Inference

 
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Lecture Notes
Lecture 01 slides [PDF]   links outline Introduction and Course Summary
Lecture 02 slides [PDF]   links outline Graph notation and representation
Lecture 03 slides [PDF]   links outline Application: Bayesian Networks (and Complexity)
Lecture 04 slides [PDF]   links outline Graph connectivity and traversal
Lecture 05 slides [PDF]   links outline Nodes, paths and cycles
Lecture 06 slides [PDF]   links outline Application: Genome Reconstruction
Lecture 07 slides [PDF]   links outline Graph features
Lecture 08 slides [PDF]   links outline Graph features (2)
Lecture 09 slides [PDF]   links outline Application: PageRank
Lecture 10 slides [PDF]   links outline Random Graphs: Erdos-Renyi random graphs
Lecture 12 slides [PDF]   links outline Random Graphs: spatially-embedded and small-world networks
Lecture 13 slides [PDF]   links outline Random Graphs: preferential-attachment models
Lecture 14 slides [PDF]   links outline Random Graphs: HOT and COLD
Lecture 15 slides [PDF]   links outline Modelling with Graphs, and Artificial Neural Networks
Lecture 16 slides [PDF]   links outline Operations on graphs (unary operators)
Lecture 17 slides [PDF]   links outline Operations on graphs (binary operators)
Lecture 18 slides [PDF]   links outline Application: Graph Matching
Lecture 19 slides [PDF]   links outline Shortest paths (Floyd-Warshall algorithm)
Lecture 20 slides [PDF]   links outline Path algebras
Lecture 21 slides [PDF]   links outline Path-problem algorithms
Lecture 22 slides [PDF]   links outline Network Topology Measurement
Lecture 23 slides [PDF]   links outline Network Sampling
Lecture 24 slides [PDF]   links outline Network Tomography
Lecture 25 slides [PDF]   links outline Network Topology Inference
Lecture 26 slides [PDF]   links outline Revision

Matthew Roughan

Last modified: Tue Aug 9 14:56:39 2022