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)

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:
    • ErdosRenyiDegDist.nlogo
    • GiantComponent.nlogo
    • SmallWorldWS.nlogo
    • RAndPrefAttachment.nlogo
  • 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.
    • Broido, A. D., & Clauset, A. (2019). Scale-free networks are rare. Nature communications, 10(1), 1017.
    • Holme, P. (2019). Rare and everywhere: Perspectives on scale-free networks. Nature communications, 10(1), 1016.

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.
  • Aparício, D., Ribeiro, P., & Silva, F. (2016). A subgraph-based ranking system for professional tennis players. In Complex Networks VII: Proceedings of the 7th Workshop on Complex Networks CompleNet 2016 (pp. 159-171). Cham: Springer International Publishing.

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)
  • Gephi Quickstart
  • 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. (local copy)

6 - Subgraph Patterns

• 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:
  • Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U. (2002). Network motifs: simple building blocks of complex networks. Science, 298(5594), 824-827.
  • Milo, R., Itzkovitz, S., Kashtan, N., Levitt, R., Shen-Orr, S., Ayzenshtat, I., ... & Alon, U. (2004). Superfamilies of evolved and designed networks. Science, 303(5663), 1538-1542.
  • Pržulj, N. (2007). Biological network comparison using graphlet degree distribution. Bioinformatics, 23(2), e177-e183.
  • Ribeiro, P., & Silva, F. (2014). G-tries: a data structure for storing and finding subgraphs. Data Mining and Knowledge Discovery, 28(2), 337-377. [pre-print version]
  • Ribeiro, P., Paredes, P., Silva, M. E., Aparicio, D., & Silva, F. (2021). A Survey on Subgraph Counting: Concepts, Algorithms, and Applications to Network Motifs and Graphlets. ACM Computing Surveys (CSUR), 54(2), 1-36.

7 - 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]

8 - Creating, manipulating and analyzing networks with NetworkX

• Jupyter Notebooks:
  • Brief NetworkX Tutorial: jupyter notebook | html (auxiliary files: lesmiserables.gml / matrix.gexf)
  • Some (optional) NetworkX Exercises: jupyter notebook | html
• Other Material:
  • NetworkX | Documentation
  • igraph | Python | C/C++ | R | Mathematica
  • network-kit
  • graph-tool
  • SNAP (Stanford Network Analysis Project) | C++ | Python
  • Grape
  • Boost Graph Library (C++)
  • Benchmark of popular graph/network packages v2 (may, 2020)

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