Mathematics of Large Technological Evolving Networks
Sponsored by the UK's Engineering
and Physical Sciences Research Council.
Grants: EPSRC , EP/I016058/1, EP/I016031/1 & EP/I017321/1
Project dates: January 2011 - January 2013
Abstract
Connections are important. In studying nature, technology, commerce
and the social sciences it often makes sense to focus on the pattern of
interactions between individual components as networks. In addition,
improvements in computing power have made it possible to gather, store
and analyze large data sets, especially in the areas of fast moving
consumer goods (who bought what), telecommunications (who phoned who),
mobile devices (who travelled where) , on-line social networks (who
Twittered to who) and energy (who switched on when).
This project aims to further advance this important area by addressing
an important feature that has fo far received very little attention
from the mathematical community: technological
networks vary over
time. Indeed, such networks are common place:
- in energy: fluctuations in power consumption by users across different times of the day and seasons,
- in telecommunications: the time order of phone calls and text messages by users across time,
- in transport: the time table of train stations, airports or ports connecting cities,
- in the World Wide Web: the evolution of hyperlinks added and deleted between pages,
- in online social networks: as friends are added and deleted,
- in retail trade: networks of consumer similarity based on the
seasonality of product demand.
Further, dynamic networks have important consequences, for example, if
A
phones B today and B phones C tomorrow, then a message may pass from A
to C, but not from C to A. So there is an immediate lack of symmetry
that makes much of the existing theory obsolete. Moreover, the patterns
of connectivity that we see today may be different tomorrow. So there
is built-in uncertainty about the future. In this proposal we will
develop new mathematical techniques to study the type of dynamically
evolving networks that are relevant in the Digital Economy, allowing
researchers to discover the important players, quantify the efficiency
of a network and predict future behaviour. These ideas offer immediate
benefits outside academia, allowing us to tackle questions such as: who
are the important broadcasters or receivers of information? who should
we target our advertising campaign at? what will the network look like
next week or next year? is there any suspicious activity today? which
networks users appear to be underage? which customers are likely to
change brand loyalty? how quickly will a rumour or virus spread? what
would be the effect of changing the way that customers are charged for
network usage?
Our objectives are to develop to practical, quantitative solutions to
these issues by developing a new, underpinning mathematical framework
that leads directly to useful computer software. In order to make sure
that the results will have immediate benefit, we have put together a
team of non-academic experts who use large technological networks in
their businesses. These people will provide realistic data sets, pose
specific challenges and provide regular feedback and advice throughout
the project.
Academic Partners
 |
Dept. of Mathematics and
Statistics, University of Strathclyde |
 |
Dept. of Mathematics
and Statistics, University of Reading |
 |
Computer
Laboratory, University of Cambridge |
Industrial Partners
 |
i2,
Cambridge |
 |
DMarket
Sentinel, London |
- MOLTEN News
-
14 January 2011
Kick-off meeting
The MOLTEN kick-off meeting will take place in Reading on the 17th of February 2011.
16 January 2011
MOLTEN Web-site is launched
The public MOLTEN web-site is now available.