MAE 298, Spring 2006
Understanding Networks: Theory and Applications
Class Projects

A large component of this class is a course project. Students may work individually or in small groups. The projects will complement and extend the lecture material, and allow the student to extend ideas of networks into their research areas. The project may include simulation of a new model, analysis of data from a known network, visualization tools, developing a software platform.....


Template for final project. This is a guideline, meant to illustrate the components necessary for your report. Feel free to use section titles given, or to paraphrase. Write this report in LaTeX, or your preferred word processing environment. For your convinience the template is available as a Word doc.


A list of potential projects areas:

  • NEW: Collaborate with the developers of billmonk.com, by analyzing data on the usage of their "social currency" website. They have some interesting visualizations of their current network.

  • General techniques:

  • Random walks on different kinds of networks (scale-free, random graphs, small-worlds), and connection to PDEs.

  • Connections between Markov chains and PDEs.

  • Percolation: spreading/contact proceeses on different kinds of graphs; including a conditional probability (failure more likely if neighbor fails).

  • Analyze (theory or simulation) networks with weighted edges (flow, convergence to steady state, robustness to random edge deletion).

  • Analyze (theory or simulation) interacting networks (social network relying on email network, relying on data network, relying on physical network).

  • Extend a standard model to a context where network connectivity is dynamic.

  • Specific systems:

  • Power grid: analysis of structure; modeling of cascading failures; siesmic fragility.

  • GIS data: Analyze highway and road data; correlate data from social interaction networks with geographical location; visualize (correlate network connectivity and geographic location).

  • Distributed energy generation: hydrogen economy and location of fueling stations; dynamic demand curve and network adaptation (to make use of scarce resources or to mitigate failure).

  • Transportation: car travel versus air travel (contrast optimal network topology); subway systems (compare optimal to actual)

  • Data networks (esp. Internet): algorithms for web search, for routing, for routing with dynamic edges, etc.

  • Software networks.

  • Network visualization.

  • Ecological and biological networks.

  • Economic networks.