Network Theory and Financial Risk (2nd edition)
First published:
ISBN: 978-1-78272-432-2
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Identifying important nodes: Centrality metrics
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 fields of statistical mechanics and social network analysis have seen significant progress towards understanding the structure and functioning of complex networks. A multitude of centrality measures has been developed, each with an explicit or implicit network process in mind. When deciding which measure is best for the question at hand, it is useful to think about the assumptions of the centrality metrics in terms of processes taking place in the networks.
Borgatti (2005) identifies several stylised processes. According to this typology, a process can move through a network via geodesic paths, paths, trails or walks. Processes that travel via geodesic (shortest) paths are, for example, problems of the “travelling salesman” type – ie, they always take the shortest route between two nodes. Processes that travel via paths need not necessarily use the shortest route, but do not visit any node more than once. These can, for example, be viral infections (a person becomes immune once infected, so cannot become infected again) or the routing of Internet traffic. Processes that travel along trails do not visit any given link more than once. An example of this is the spread of
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