Complex-Network Modelling and Inference
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| Lecture 01 | ![slides [PDF]](images/slides.jpg) |
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Introduction and Course Summary | |
| Lecture 02 | |
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Graph notation and representation | |
| Lecture 03 | |
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Application: Bayesian Networks (and Complexity) | |
| Lecture 04 | |
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Graph connectivity and traversal | |
| Lecture 05 | |
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Nodes, paths and cycles | |
| Lecture 06 | |
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Application: Genome Reconstruction | |
| Lecture 07 | |
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Graph features | |
| Lecture 08 | |
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Graph features (2) | |
| Lecture 09 | |
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Application: PageRank | |
| Lecture 10 | |
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Random Graphs: Erdos-Renyi random graphs | |
| Lecture 12 | |
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Random Graphs: spatially-embedded and small-world networks | |
| Lecture 13 | |
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Random Graphs: preferential-attachment models | |
| Lecture 14 | |
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Random Graphs: HOT and COLD | |
| Lecture 15 | |
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Modelling with Graphs, and Artificial Neural Networks | |
| Lecture 16 | |
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Operations on graphs (unary operators) | |
| Lecture 17 | |
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Operations on graphs (binary operators) | |
| Lecture 18 | |
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Application: Graph Matching | |
| Lecture 19 | |
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Shortest paths (Floyd-Warshall algorithm) | |
| Lecture 20 | |
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Path algebras | |
| Lecture 21 | |
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Path-problem algorithms | |
| Lecture 22 | |
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Network Topology Measurement | |
| Lecture 23 | |
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Network Sampling | |
| Lecture 24 | |
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Network Tomography | |
| Lecture 25 | |
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Network Topology Inference | |
| Lecture 26 | |
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Revision |
Matthew Roughan
Last modified: Wed Jun 12 13:46:38 2024