Network Science 2020/2021

#0 - Network Science Course Presentation (2020/2021) [39m30s]
Course overview: general information, evaluation, learning outcomes, overview of the program

#1 - Motivation and the "small world" phenomenon [23m36s]
An introduction to Network Science: motivation and the small world phenomenon;

Class #2 (16 Feb)

#2 - Emergence of Network Science [36m33s]
Contributing factores to the emergence of network science; mining an learning with graphs: an overview of related tasks and topics

#3 - Brief Introduction to Graph Theory and Network Vocabulary [35m33s]
Brief introduction to graph theory and network vocabulary: links/edges, networks/graphs, self-loops, multi-graphs, simple graphs, undirected and directed networks, edge and node attributes (weigth, rank, type), degree distribution; multiplex networks, temporal networks, cliques, components (and giant component), bipartite networks (and projections)

Class #3 (22 Feb)

#4 - Measuring Graphs and Results on Real Networks [54m27s]
Networks properties: degree distribution, paths, distance, diameter, average path length, clustering coefficient, connected components and gian component; properties of real networks: results for MSN and PPI; network datasets.

Class #4 (23 Feb)

#5 - Erdös-Renyi Random Graph Model [56m39s]
The Erdös-Renyi random graph model and its topological properties: degree distribution, cluster coefficient, expansion, path length and the emergence of a giant component; comparison of Erdös-Renyi with real-world graphs; usage of NetLog for visualization.

#6 - Small-World Random Graph Model [29m11s]
Milgram's small world experiment; clustering and edge locality, the Watts-Strogatz model and the intermediate step between purely regular and purely random networks.

Class #5 (1 Mar)

#7 - Power Laws and Preferential Attachment [1h35m09s]
Power-laws in real-world degree distributions and typical exponents; power-law functions and their characteristics; scale-free networks; preferential attachment: origins, models, visualization and characteristics.

Class #6 (2 Mar)

#7 - Power Laws and Preferential Attachment [1h35m09s]
Fitting power laws: simple binning, logarithmic binning, cumulative distributions; power-laws in the tail of real distributions; other distributions.

Class #7 (8 Mar)

#8 - Node Centrality [1h10m49s]
Motivation: node importance and ranking; degree centrality and why it's not enough; betweenness centrality; closeness and harmonic centrality; eigenvector centrality and variants (e.g. bonachich); extension to directed and weighted networks.

Class #8 (9 Mar)

#9 - Gephi Tutorial [41m12s]
Using Gephi, an open-source network analysis and visualization software: GUI, plugins, changing node and edge appearance, graph layouts, computing metrics and statistics, filtering. Exploring two datasets: a Facebook ego network and the openflights dataset.

Class #9 (15 Mar)

#10 - Link Analysis and PageRank [1h44m03s]
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Class #10 (16 Mar)

Supporting the 1st Homework
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Class #11 (22 Mar)

#11 - Roles and Community Structure in Networks [1h37m39s]
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Class #12 (23 Mar)

Pratical: NetworkX

Network Science 2020/2021

Videos

Quick links to all published videos

Detailed Description / Summaries

Class #1 (15 Feb)