The past decade has seen a convergence of social and technological networks, with systems such as the World Wide Web characterized by the interplay between rich information content, the millions of individuals and organizations who create it, and the technology that supports it. This course covers recent research on the structure and analysis of such networks, and on models that abstract their basic properties. Topics include combinatorial and probabilistic techniques for link analysis, centralized and decentralized search algorithms, network models based on random graphs, and connections with work in the social sciences.
In mathematics and physics, a small-world network is a type of mathematical graph in which most nodes are not neighbors of one another, but most nodes can be reached from every other by a small number of hops or steps. A small world network, where nodes represent people and edges connect people that know each other, captures the small world phenomenon of strangers being linked by a mutual acquaintance.
J. Leskovec, and C. Faloutsos. Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, page 631--636. New York, NY, USA, ACM, (2006)
J. Leskovec, J. Kleinberg, and C. Faloutsos. KDD '05: Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, page 177--187. New York, NY, USA, ACM Press, (2005)