Lectures (Social and technological networks)

Topic 1 : Introduction

• Slides

• IPython notebook

• Sample Problems

• Reading

  • Easley-Kleinberg, Chapter 2.
  • Asymptotic notations (Big O and Big Omega)
  • Graph Theory wikibook -- basic definitions (optional)

• Additional (optional) reading

  • None for this lecture.

Topic 2 : Random graphs

• Slides
• ipython notebook

Topic 3: Random graphs continued

• Slides
• Reading:
  • Kleinberg's notes on random graphs

Topic 4 : Power law networks

• Slides
• ipython notebook

• Reading:

  • Kempe 2011 -- Chapter 6, Beginning to Sec 6.1
  • Kleinberg & Easley 2010 -- Chapter 18

• Additional (optional) readings.

  • Rest of Kempe 2011, Chapter 6.
  • Graphs over time.
  • Mean field theory of complex networks.
  • Power law relationships of Internet topology.
  • Brief history of generative models for power law and log-normal distributions

Topic 5 : Small world networks

• Slides

• Reading

  • Collective Dynamics of Smalls world networks (accessible from university network or vpn)
  • Navigation in a small world (accessible from university network or vpn)
  • Kempe 2011 Chapter 7.
  • Kleinberg & Easley 2010 – Chapter 20.

• Additional (optional) readings

  • Complex networks and decentralized search
  • The Small-World Phenomenon: An Algorithmic Perspective
  • Distributed compact routing using spatial distributions
  • Power of lookahead in randomized p2p networks

Topic 6 : Strong and weak ties

• Slides

• Reading

  • Kleinberg & Easley 2010 Chapter 3 (including advanced material), Chapter 4, upto Sec 4.4.
  • Tie strength in mobile nets

• Additional (optional) reading.

  • Rest of Kleinberg & Easley 2010 Chapter 4
  • Granovetter, Strength of Weak ties

Topic 7 : Web graphs and Ranking pages

• Slides
• ipython notebook
• Reading
  • Kleinberg & Easley 2010 – Chapter 13 & 14.
• Additional (optional) reading:
  • Network of global corporate control. Vitali et al.

Topic 8 : Spectral analysis of ranking algorithms

• Slides

• Reading

  • Kleinberg & Easley 2010 Chapter 14 (including advanced material)

• Additional (optional) reading:

  • Kempe 2011. Chapter 2.
  • Perron's theorem on wikipedia
  • Topic sensitive pagerank
  • Making eigen vector based reputation systems robust to collusion

Topic 9 : Spectral graph theory

• Slides
• ipython notebook

Topic 10 : Cascades

• Slides
• Reading:
  • Kleinberg & Easley 2010 Chapter 19: cascades.

Topic 11 : Influence maximisation (submodular optimisation)

• Slides

• Reading

  • Kleinberg & Easley 2010 Chapter 19.
  • Kempe 2011 Chapter 8.6
  • Maximizing spread of influence

• Additional (optional) reading:

  • Rest of Kempe 2011 Chapter 8.

Topic 12 : Epidemics and diffusion

• Slides
• Reading
  • Kleinberg & Easley 2010 Chapter 21.
• Additional (optional) reading
  • Kleinberg & Easley 2010 Chapter 21: Additional materials.

Topic 13 : Community detection

• Slides
• Reading
  • Kempe 2011 Chapter 3. Upto Sec. 3.5.2.
  • C. Aggarwal: Data Mining Textbook [acccessible from University network] Clustering: Sections 6.3, 6.4, 6.6
• Additional (optional) reading
  • Newman: Fast clustering for detecting community
  • Bansal: Correlation clustering

Topic 14: Centrality, treeness and miscellaneous

• Slides

• Additional (optional) reading:

  • Wikipedia page on centrality