Handouts

0 - The Network Science Course

Slides:
- Information about the course

1 - Fundamentals of Network Science

Videos:
- Motivation and the "small world" phenomenon [23m36s]
- Emergence of Network Science [36m33s]
- Brief Introduction to Graph Theory and Network Vocabulary [35m33s]
Slides:
- An introduction to Network Science
Other Material:
- Network Science - Chapter 1: Introduction (A. Barabasi)
- Network Science - Chapter 2: Graph Theory (A. Barabasi)
- Networks, Crowds and Markets - Chapter 1: Overview (D. Easley and J. Kleinberg)
- Networks, Crowds and Markets - Chapter 2: Graphs (D. Easley and J. Kleinberg)
- The "new" science of networks (D. Watts)

2 - Measuring Networks and Random Graph Models

Videos:
- Measuring Graphs and Results on Real Networks [54m27s]
- Erdös-Renyi Random Graph Model [56m39s]
- Motivation and the "small world" phenomenon [29m11s]
- Power Laws and Preferential Attachment [1h35m09s]
Slides:
- Measuring Networks and Random Graph Models
Other Material:
- Network Science - Chapter 3: Random Networks (A. Barabasi)
- Network Science - Chapter 4: The Scale-Free Property (A. Barabasi)
- Network Science - Chapter 5: The Barabasi-Albert Model (A. Barabasi)
- Networks, Crowds and Markets - Chapter 18: Power Laws and Rich-Get-Richer Phenomena (D. Easley and J. Kleinberg)
- Networks, Crowds and Markets - Chapter 20: The Small-World Phenomenon (D. Easley and J. Kleinberg)
- NetLogo Visualizations:
- Some papers mentioned in lectures:
  - Leskovec, J., & Horvitz, E. (2008, April). Planetary-scale views on a large instant-messaging network. In Proceedings of the 17th international conference on World Wide Web (pp. 915-924).
  - Erdős, P., & Rényi, A. (1960). On the evolution of random graphs. Publ. Math. Inst. Hung. Acad. Sci, 5(1), 17-60.
  - Travers, J., & Milgram, S. (1969). An experimental study of the small world problem. Sociometry, 32(4), 452-443.
  - Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’networks. Nature, 393(6684), 440-442.
  - Barabási, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509-512.
  - Clauset, A., Shalizi, C. R., & Newman, M. E. (2009). Power-law distributions in empirical data. SIAM review, 51(4), 661-703.

3 - Centrality

Videos:
- Node Centrality [1h10m49s]
Slides:
- Node Centrality
Other Material:
- Centrality Measures and Link Analysis (G. Mateos)
- Wikipedia: Centrality
- A. Saxena, S. Iyengar (2020). Centrality measures in complex networks: A survey. arXiv:2011.07190.

4 - Network Analysis and Visualization with Gephi

Videos:
- Gephi Tutorial [41m11s]
Slides:
- Network Analysis and Visualization with Gephi
Other Material:
- Gephi – Free and Open-Source Network Analysis Platform (main webpage for download)
- Learn how to use Gephi (several tutorials available)
- Gephi – Introduction to Network Analysis and Visualization (tutorial by M. Grandjean)
- Gephi Plugins
- Some datasets:
  - Datasets used in class: facebook.gephi | airports.csv | routes.csv | unicode_nodes.csv | unicode_edges.csv |
  - Many other datasets: see some websites with network datasets at the useful links page

5 - Link Analysis

Videos:
- Link Analysis and PageRank [1h44m03s]
Slides:
- Link Analysis: PageRank
Other Material:
- Centrality Measures and Link Analysis (G. Mateos)
- Wikipedia: HITS algorithm
- Wikipedia: PageRank
- NetLogo Visualizations:
  - PageRank.nlogo
- Some papers mentioned in lectures:
  - Broder, A., Kumar, R., ... & Wiener, J. (2000). Graph structure in the web. Computer networks, 33(1-6), 309-320.
  - Kleinberg, J. M. (1999). Authoritative sources in a hyperlinked environment. Journal of the ACM (JACM), 46(5), 604-632.
  - Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: Bringing order to the web. Stanford InfoLab.

6 - Roles and Community Structure in Networks

Videos:
- Roles and Community Structure in Networks [1h37m39ss]
Slides:
- Community Structure in Networks (selected slides from J. Leskovec and G. Mateos)
Other Material:
- Network Science - Chapter 9: Communities (A. Barabasi)
- Networks, Crowds and Markets - Chapter 3: Strong and Weak Ties (D. Easley and J. Kleinberg)
- Some papers mentioned in lectures:
  - Henderson et al (2012). Rolx: structural role extraction & mining in large graphs. Proceedings of the 18th ACM SIGKDD international conference on Knowledge Discovery and Data Mining (pp. 1231-1239).
  - Fortunato, S. (2010). Community detection in graphs. Physics reports, 486(3-5), 75-174.
  - Fortunato, S., & Hric, D. (2016). Community detection in networks: A user guide. Physics reports, 659, 1-44.
  - Yang, J., & Leskovec, J. (2012). Defining and evaluating network communities based on ground-truth. In Proceedings of the ACM SIGKDD workshop on mining data semantics (pp. 1-8).
  - Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment, 2008(10), P10008. [Louvain Algorithm]

7 - Subgraph Patterms

Videos:
- Subgraphs as Fundamental Ingredients of Complex Networks [2h11m21s]
Slides:
- Subgraphs as Fundamental Ingredients of Complex Networks
Other Material:
- gtrieScanner (P. Ribeiro)
- Some articles to read: