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CATEGORIES:Cambridge Networks Network (CNN)
SUMMARY:CNN seminar - January - Speaker to be confirmed
DTSTART;TZID=Europe/London:20120126T140000
DTEND;TZID=Europe/London:20120126T150000
UID:TALK35814AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/35814
DESCRIPTION:*Vincenzo Nicosia* (Computer Laboratory\, Universi
ty of Cambridge\, UK and Laboratory on Complex Sys
tems\, Scuola Superiore di Catania\, Italy):\n\n"C
ontrolling centrality in complex networks"\n\nand\
n\n*Eiko Yoneki* (Computer Laboratory\, University
of Cambridge\, UK):\n\n"On Joint Diagonalisation
for Dynamic Network Analysis"\n\n*Abstracts:*\n\n*
Controlling centrality in complex networks*\n\nNod
e and edge centrality have a pivotal importance in
the study and characterization of complex network
s\, and nowadays centrality measures are widely us
ed to identify influential individuals in social g
roups\, to rank Web pages by popularity\, and even
to determine the impact of scientific research. M
any different structural properties have been used
to assess the importance of nodes\, but in most o
f the cases the centrality of every single node cr
ucially depends on the entire pattern of connectio
ns. Therefore\, the usual approach is to compute n
ode centralities once the network structure is ass
igned. We discuss here a solution to the so-called
"inverse centrality problem"\, which consists int
o controlling the centrality scores of the nodes b
y opportunely acting on the structure of a given n
etwork. In particular\, we focus our attention on
spectral centrality measures and we show that ther
e exist particular subsets of nodes\, called contr
olling sets\, which can assign any prescribed set
of centrality values to all the nodes of a graph\,
by cooperatively tuning the weights of their out-
going links. We found that many large networks fro
m the real world have surprisingly small controlli
ng sets\, containing even less than 5-10% of the n
odes. Consequently\, the rankings obtained by spec
tral centrality measures should be taken into acco
unt with extreme care\, since they can be easily m
anipulated and even distorted by small groups of m
alicious nodes acting cooperatively.\n\nReferences
: [1] V. Nicosia\, R. Criado. M. Romance\, G. Russ
o and V. Latora "Controlling centrality in complex
networks" Scientific Reports 2\, 218 (2012)\, doi
:10.1038/srep00218 http://www.nature.com/srep/2012
/120111/srep00218/full/srep00218.html\n\n \n\n*On
Joint Diagonalisation for Dynamic Network Analysis
*\n\nJoint diagonalisation (JD) is a technique use
d to estimate an average eigenspace of a set of ma
trices. Whilst it has been used successfully in ma
ny areas to track the evolution of systems via the
ir eigenvectors\; its application in network analy
sis is novel. The key focus is the use of JD on ma
trices of spanning trees of a network. This is esp
ecially useful in the case of real-world contact n
etworks in which a single underlying static graph
does not exist. The average eigenspace may be used
to construct a graph which represents the `averag
e spanning tree' of the network or a representatio
n of the most common propagation paths. We then ex
amine the distribution of deviations from the aver
age and find that this distribution in real-world
contact networks is multi-modal\; thus indicating
several modes in the underlying network. These mod
es are identified and are found to correspond to p
articular times. Thus JD may be used to decompose
the behaviour\, in time\, of contact networks and
produce average static graphs for each time. This
may be viewed as a mixture between a dynamic and s
tatic graph approach to contact network analysis.\
n\nhttp://arxiv.org/abs/1110.1198
LOCATION:Keynes Hall in Kings College
CONTACT:Petra Vertes
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