Introduction to modern-day networked technologies such as wireless, social, and economic networks. Analysis of networked technologies using analytical and engineering techniques such as optimization, game/auction theory, graph analysis, and learning as applied to networked technologies. Introduction to the basics of these techniques and their applications in networked systems. Development of a network science for solving practical problems pertaining to various networked systems such as smartphones, Wiki, Facebook, recommendation systems, economic network, or online video/music streaming software. Pre: 2704 (C- or better) (3H, 3C).

The past decade witnessed major breakthroughs in communications and networking technologies that include the rise of smartphones, social networking websites, and other online tools such as YouTube, NetFlix, or Wikipedia. While our undergraduate students have been keeping up with these technologies and interacting with them almost daily, our curriculum has yet to incorporate the mathematical machinery that allows such technologies to operate. This machinery is essentially a mix of various disciplines that include networking, optimization theory, operations research, and economics. Analyzing and understanding networks is simply no longer possible without resorting to such techniques. Therefore, the primary goal of this course is to introduce these analytical techniques while taking an application oriented view. In particular, we will pose several questions on the technologies that surround our students (e.g., how to influence people on social networks, how does a smartphone work, how do wireless data plans get designed), and, while answering those questions, we will provide a thorough introduction to the underlying engineering and analytical principles. This course will be very valuable to provide our undergraduate students with a truly hands-on, minds-on approach to understanding networking technologies as it continues to proliferate in our daily lives. In particular, it will allow them to clearly link theory and practice in the area of networked systems.

- 1. Define core concepts and fundamental methodologies relevant to communications, the Internet, social networks, recommendation systems, and wireless networks.
- 2. Formulate and solve practical networking problems using analytical tools and frameworks such as optimization, graph theory, network economics, and learning algorithms.
- 3. Develop inference and probability techniques to analyze engineering problems such as network recommendation systems.
- 4. Develop algorithms and techniques, such as distributed power control, Pagerank, and network utility maximization, for analyzing and optimizing Internet, social, and wireless networks.
- 5. Describe the connection between theoretical communications and networking concepts and existing online technologies, such as Google or Facebook.

## Topic |
## Percentage of Course |

Graph theory basics; Pagerank algorithm, and application to web search | 10% |

Basics of optimization, and applications in wireless networks | 15% |

Auction theory and applications to online advertising | 10% |

Recommendation systems, Bayesian learning, convex optimization, neighborhood models, and applications to online reviews. | 15% |

Social influence, network algorithms, network and graph properties, and applications to social networking. | 20% |

Small world, graph partitioning, random graphs, and applications to internet networks. | 10% |

Voting systems, bargaining games, and applications to Wikipedia. | 10% |

Wireless network economics, network utility maximization, and fairness metrics. | 10% |

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