Six Degrees: The New Science of Networks (Collective Dynamics Group ISERP -- Columbia University) Course Syllabus

Six Degrees: The New Science of Networks (Sociology, W3233, Spring 2006) Original URL: http://cdg.columbia.edu/cdg/courses/spring06/sixDegrees/syllabus.jsp

W3233 -- Networks and Complexity in Social Systems (Collective Dynamics Group ISERP -- Columbia University) Course Syllabus

The Networks and Complexity in Social Systems course commences with an overview of the nascent field of complex networks, dividing it into three related but distinct strands: Statistical description of large scale networks, viewed as static objects; the dynamic evolution of networks, where now the structure of the network is understood in terms of a growth process; and dynamical processes that take place on fixed networks; that is, "networked dynamical systems". (A fourth area of potential research ties all the previous three strands together under the rubric of co-evolution of networks and dynamics, but very little research has been done in this vein and so it is omitted.) The remainder of the course treats each of the three strands in greater detail, introducing technical knowledge as required, summarizing the research papers that have introduced the principal ideas, and pointing out directions for future development. With regard to networked dynamical systems, the course treats in detail the more specific topic of information propagation in networks, in part because this topic is of great relevance to social science, and in part because it has received the most attention in the literature to date.

Viscous Flow and Fluid Dynamics

Exam questions and solutions for a third year maths course. Diagrams for the questions are all together in the support.zip file, as .eps files

Climate Dynamics

A Masters level module

WAIS seminar: Provenance Analytics and Crowdsourcing

Wednesday 26th March 2014 Speaker(s): Dr Trung Dong Huynh Organiser: Dr Tim Chown Time: 26/03/2014 11:00-11:50 Location: B32/3077 File size: 349Mb Abstract Understanding the dynamics of a crowdsourcing application and controlling the quality of the data it generates is challenging, partly due to the lack of tools to do so. Provenance is a domain-independent means to represent what happened in an application, which can help verify data and infer their quality. It can also reveal the processes that led to a data item and the interactions of contributors with it. Provenance patterns can manifest real-world phenomena such as a significant interest in a piece of content, providing an indication of its quality, or even issues such as undesirable interactions within a group of contributors. In this talk, I will present an application-independent methodology for analysing provenance graphs, constructed from provenance records, to learn about such patterns and to use them for assessing some key properties of crowdsourced data, such as their quality, in an automated manner. I will also talk about CollabMap (www.collabmap.org), an online crowdsourcing mapping application, and show how we applied the approach above to the trust classification of data generated by the crowd, achieving an accuracy over 95%.