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„If we tie a lot of cherry-peppers on a string, they’ll make a pepper-wreath.
However, if we don’t tie them on a string, they won’t make a wreath. Although it’s the same amount of peppers, just as red and just as hot. But still no wreath.
Does it only lie in the string? No, it doesn’t. That string, as we all know, is an incidental, third-rate thing.
Then what?
People capable of brooding over it and taking care not to let their mind wander about, but keep them on the right track may get a scent of eternal verities.”
Örkény István: The meaning of life
Translated by N. Ullrich Katalin
Cattle trade flow analysis
Static and dynamic properties of the network were defined.
Degree: the number of relations or edges of the nodes
In-Degree: incoming connections
Out-Degree: outgoing connections
Degree has generally been extended to the sum of weights when analysing weighted networks and labelled as node strength: number of animals moved.
To understand the relative importance of different hubs in cattle flow, we had to apply the Centrality concepts of network analysis.
Structural properties
Based on the degree distribution the Hungarian cattle holdings network shows scale-free network characteristics: it is a network whose degree distribution follows a power law.
The most vulnerable points of a network are not necessarily their largest hubs. To extract information on the nodes playing central role in the network, different centrality measures were calculated: betweenness, closeness, authority and hub centralities were defined.
The degree exponent for the Hungarian cattle holdings network is 2,24.
There is a big difference between those centralities: in case of the authority and hub centrality a central node can be any node in the network, but in the case of betweenness and closeness centralities (as the names indicate) the central nodes can not be source-vertex or sink-vertex. The vertices of high betweenness centrality value are usually logistic centres, transloading places or major livestock farms.
Therefore it shows small world properties, meaning that only few steps are needed to get from a random point to another random point, having large implications in case of spreading of different diseases.
These nodes have important role in epidemiological investigations, because of the high risk of cross-infections.
Outputs: control plans
Dynamic properties
The results presented above all contribute to network analysis based risk based control plans.
The simplest approach is to observe the number of animals moving per month. The results indicated, that the trade becomes very active in June-July with a peak of activity at the end of the year.
The trend of increasing activity in the second half of the year seems to be stable. This should have an impact on the control time schedules, assigning increased control frequencies to those periods.
(1) Control plan aiming to control the epidemiological, hygiene, animal welfare, etc. properties (‘food chain safety control’), as defined by the Regulation 882/2004/EC
(2) Control plan aiming to control the requirements posed by Regulation 1760/2000/EC on animal identification (‘animal identification control’)
(3) Control plan for ‘critical infrastructures’ (critical from food security perspectives).
It can be derived from the results, that apart from small differences, the network characteristics are quite stable over time, allowing for predictions at the whole network level.
We have selected top 5 nodes on the betweenness centrality rank list, and plotted the monthly betweenness centrality values, to see, when the nodes played central role in the network in the past 3 years.
The results let us see a very volatile nature of the holdings in relation to betweenness centrality values.
CHAIN
Leonhard Euler
(1707-1793)
1967
2016
Network of animal farms
Network of countries
Fight against terrorism –
Protecting the critical infrastructures
RASFF notifications
Which farm is the most probable place being a hub in future epidemies?
Risk analysis from critical infrastructure point of view
Usual food network
risk analysis
Which network science measures fit reality the most?
No obligation for internal traceability nor for electronic databases, however there is potentially enormous amount of data to explore
Our aim is to protect this network
but on the other hand, if we choose few major hubs and take them out of the network, it simply falls apart and is turned into a set of rather isolated graphs.
Scale-free networks: allows for a fault tolerant behaviour
Export-import (trade)
Same food network
different outcomes for risk assessment
Which trade routes are the most risky?
Ercsey-Ravasz, M., Toroczkai, Z., Lakner, Z., & Baranyi, J. (2012). Complexity of the International Agro-Food Trade Network and Its Impact on Food Safety. (V. Colizza, Ed.)PLoS ONE, 7(5), e37810. doi:10.1371/journal.pone.0037810
Elaboration of spreading models
different vulnerable points
NETWORK
SCIENCE
Ákos Jóźwiak, NÉBIH
BARABÁSI
1999
Research on links of homepages: Internet constists of few large hubs and a lot of small periferic sites.
Modeling complex networks as random graphs
Party: random connections
JOZWIAKA@NEBIH.GOV.HU
Every point can be reached from any point
Physicists: phase transition (e.g. water freezes)
Sociologists: a community is formed
The scale-free name captures the lack of an internal scale, a consequence of the fact that nodes with widely different degrees coexist in the same network.
This feature distinguishes scale-free networks from lattices, in which all nodes have exactly the same degree, or from random networks, whose degrees vary in a narrow range.
This divergence is the origin of some of the most intriguing properties of scale-free networks, from their robustness to random failures to the anomalous spread of viruses.
All follow power law distribution, and are very similar to each other
Scale-free networks have small world properties.
The low-degree nodes form dense subgraphs, inteconnected by large hubs.
Scale-free networks are very fault-tolerant towards random failures (randomly removing a node will have no significant effect on the network structure).
However, they are very sensitive to targeted attacks: removing the hubs will result in quick collapsing.
RANDOM
Source: Albert-László Barabási: Network Science
The city of Königsberg in Prussia (now Kaliningrad, Russia) was set on both sides of the Pregel River, and included two large islands which were connected to each other and the mainland by seven bridges.
Euler proved that the problem has no solution.
How graphs
To devise a walk through the city that would cross each bridge once and only once, with the provisos that:
More and more pairs formed
Pál Erdős
Alfréd Rényi
Nodes:
(1) cattle exporter countries
(2) the importers, buying living cattle from Hungary
(3) the various economic organizations (e.g. farms, slaughterhouses, logistic/distribution centers, markets, etc...)
Edges:
the cattle-flows between vertices
When the number of connections equals the number of people at the party, something special happens
"
1929