- Literature - Oct 7 ’Do what you love’ mantra makes us feel like failures
- Physics - Oct 6 Laser- wielding physicists seize control of atoms’ behavior
- Physics - Oct 6 3Q: The massive impact of neutrino research
- Pedagogy - Oct 6 Cornell among top 100 adoption- friendly workplaces
- Physics - Oct 6 Sussex physicists applaud Nobel Prize- winner and collaborator
- Business - Oct 6 Keeping the supply chain flowing
- Law - Oct 6 Thomas J. Miles appointed dean of University of Chicago Law School
- Medicine - Oct 6 National research network set on venous thromboembolism
- Physics - Oct 6 Neutrinos scoop the Nobel Prize in Physics 2015
- Environmental Sciences - Oct 6 Embrace the chaos: Predictable ecosystems may be more fragile
- Medicine - Oct 6 What we can solve
- Life Sciences - Oct 6 Calling for help: damaged nerve cells communicate with stem cells
- Astronomy - Oct 6 Aliens observed
- Medicine - Oct 6 LGBTQ older adults in Seattle/King County face higher health risks
- Mechanical Engineering - Oct 6 The dynamics of evaporative patterning
- Medicine - Oct 6 New 'Weill Cornell Medicine' name announced
How quickly things spread
Understanding the spread of infectious diseases in populations is the key to controlling them. If the UK was facing a flu pandemic, how could we measure where the greatest spreading risk comes from? This information could help inform decisions on whether to impose travel restrictions or close schools.
We would like to offer our metrics to the research community as a better tool to measure behaviour in dynamic networks."—Hyoungshick Kim
Think of the patterns of human that can spread infectious disease; you might be breathed on by a hundred people a day in meetings, on public transport and even in the street. These interactions create a highly dynamic network, in which new nodes ( points), are added to the graph, some existing ones are removed, and in which edges (the lines that join the nodes) come and go too.
These are difficult concepts to grasp and the spread of diseases is just one of the many examples of visualising how networks rapidly spread into a complex mass of interactions.
Most analyses and models have assumed that networks are static, typically represented in graph form as a number of nodes connected by edges. For example, if a local council were to monitor the flow of traffic through a city, the roads would be modelled as a network and capacities would be assigned to the edges, which represent the number of lanes on the roads. Static network models would apply a network flow equation to determine the maximum traffic between any given pair of points.
Although this model would discover the maximum number of cars that can travel through a city in a single wave – if the cars all leave at the same time and get to any point with no delay – it would not be capable of plotting the time it would take for cars to travel and if cars delayed their departure.
Now, scientists at the University of Cambridge’s Computer Laboratory have taken the understanding of standard graph theory one step further by designing a model that can plot the effects of mobility and interaction with the use of a time-ordered graph.
“We would like to offer our metrics to the research community as a better tool to measure behaviour in dynamic networks,” said lead author Hyoungshick Kim, a PhD student in Professor Ross Anderson’s research group.
The time-ordered graph reduces the complexity of a dynamic network and applies it to a static network by using directed flows. Directed flows allow for network properties to be extended; such as betweenness, which measures the influence a node has over the spread of information through the network (eg how influential a person is within a social network).
For example, in epidemiology, some possibly infective between individuals are long term (friends, family) but many are fleeting (people in the street or the market place). Their relative importance may vary. The new model can be used to identify places or people that are the most influential for epidemics.
Last job offers
- Veterinary Science - 5.10
Amtstierärztin / Amtstierarzt als Abteilungsleitung
- Administration/Government - 5.10
Leitung des Bergischen Veterinär- und Lebensmittelüberwachungsamtes (m/w)
- Chemistry - 5.10
Dozentin / Dozent Chemie für Umweltingenieure (100 %)
- Social Sciences - 2.10
Senior Projektleiter/in F&E (80%)
- Business/Economics - 2.10
Junior Researcher VWL (60-80%) / Wissenschaftliche/r Mitarbeiter/in (60-80%)
- Social Sciences - 2.10
Wissenschaftliche/r Mitarbeiter/in 60%
- Life Sciences - 6.10
Full Professor / Associate Professor in biomolecular solution-state NMR spectroscopy (1, 0 fte)
- Business/Economics - 6.10
Associate professor in Data Science
- Arts and Design - 6.10
Universitätsprofessorin / Universitätsprofessor für das Fach Klavier und Klavierdidaktik (BV gem. § 98...
- Law/Forensics - 2.9
Universitätsprofessur für Smart Grids am Institut für Vernetzte und Eingebettete Systeme
- Pedagogy/Education Science - 6.10
Professur für, Fachdidaktik Geschichte und Sozialkunde, (W3)
- Literature/Linguistics - 6.10
- Administration/Government - 6.10
Academic Faculty Position in Public Policy
- Law/Forensics - 6.10
Academic Faculty Position in Law and Public Policy
- History/Archeology - 6.10
International Studies & History - Assistant, Associate, or Full Professor (AA14240)
- History/Archeology - 5.10