Analysis of Social and Information Networks 2020/2021
Videos
Quick links to all published videos
 | Playlist with all the videos |
- Chapter 1 - Fundamentals of Network Science
- #1 - Motivation and the "small world" phenomenon [23m36s]
- #2 - Emergence of Network Science [36m33s]
- #3 - Brief Introduction to Graph Theory and Network Vocabulary [35m33s]
- Chapter 2 - Measuring Networks and Random Graph Models
- #4 - Measuring Graphs and Results on Real Networks [54m27s]
- #5 - Erdös-Renyi Random Graph Model [56m39s]
- #6 - Small-World Random Graph Model [29m11s]
- #7 - Power Laws and Preferential Attachment [1h35m09s]
- Chapter 3 - Node Centrality
- #8 - Node Centrality [1h10m49s]
- Practical - Graph Visualization and Exploration with Gephi
- #9 - Gephi Tutorial [41m12s]
- Chapter 4 - Link Analysis and PageRank
- #10 - Link Analysis and PageRank [1h44m03s]
- Chapter 5 - Roles and Community Structure in Networks
- Chapter 6 - Subgraphs as Fundamental Ingredients of Complex Networks
- Chapter 7 - Diffusion and Cascading Behavior
- #13 - Diffusion and Cascading Behavior - Part 1 (Decision Based Models) [58m59s]
- #14 - Diffusion and Cascading Behavior - Part 2 (Probabilistic Models) [1h29m38s]
- Chapter 8 - Network Construction
- #15 - Network Construction [1h34m41s]
Detailed Description / Summaries
Class #1 (15-19 Feb)
Class #2 (22-26 Feb)
An introduction to Network Science: motivation and the small world phenomenon
Contributing factors to the emergence of network science; mining an learning with graphs: an overview of related tasks and topics.
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 (1-5 Mar)
Networks properties: degree distribution, paths, distance, diameter, average path length, clustering coefficient, connected components and giant component; properties of real networks: results for MSN and PPI; network datasets.
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.
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 #4 (8-12 Mar)
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; fitting power laws: simple binning, logarithmic binning, cumulative distributions; power-laws in real distributions; other distributions.
Class #5 (15-19 Mar)
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 #6 (22-26 Mar)
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 #7 (29 Mar - 02 Apr)
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Class #8 (12-16 Apr)
(to be completed...)
Class #9 (19-23 Apr)
(to be completed...)