Network Theory and Financial Risk (2nd edition)
First published:
ISBN: 978-1-78272-432-2
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Financial cartography: Network layouts
Preface
Acknowledgements
An introduction to financial networks
Creating and describing networks
Identifying important nodes: Centrality metrics
Uncovering latent structure: Clustering and community detection
Finding hidden links: Projection networks
Estimating unknown links: Network reconstruction
Fast insights: Visualising networks
Financial cartography: Network layouts
Brass tacks: Complexity reduction
Financial stability and systemic risk
Design and simulation of financial market infrastructures
Monitoring and stress testing financial market infrastructures
Financial crime and conduct risk
Financial markets
References
The placement of nodes in a network visualisation can greatly affect how informative the visualisation is. For example, the placement of nodes in Figure 7.3 emphasises the importance of the Danish krone, and the other nodes are arranged in a circle sorted by their correlation to DKK, making the strength of relationships easy to see and compare. One strategy utilised in various layout algorithms to best show network structure is to place the nodes so as to minimise the number of link crossings. Depending on the type of network and the goals of the visualisation, however, other properties may be more desirable. For instance, in a network with a strong community structure, the most important factor may be closeness of nodes in the same community. This chapter discusses many different layout algorithms for network visualisation, with real data examples of each.
We will illustrate the layout algorithms presented in this chapter using a network of annual remittance data (focusing on the year 2018) compiled by the World Bank.11 See https://www.worldbank.org/en/topic/migrationremittancesdiasporaissues/brief/migration-remittances-data. The network contains 212 nodes, which are mostly
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