Network Theory and Financial Risk (2nd edition)
First published:
ISBN: 978-1-78272-432-2
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Uncovering latent structure: Clustering and community detection
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
Community detection for networks refers to grouping the nodes of a network in such a way that the nodes within a group are more similar to each other or more connected to each other than to nodes in other groups. We have already seen a few methods for grouping nodes, core–periphery classification and strong and weak components, in Chapter 2. Community detection aims to uncover other types of network structure and, unlike the methods previously presented, may depend on link properties as well as link structure. Community detection is useful in financial networks – for example, in portfolio optimisation. This chapter will present many different algorithms for community detection, each with background on the methodology and examples with real data.
CLIQUE-BASED METHODS
A clique is a subset of the nodes in a network such that the corresponding subnetwork is complete. For instance, any subset of the nodes in a complete network form a clique. A maximum clique is the largest clique in a network and may represent an important community within the network. For example, in a perfect core–periphery network, the maximum clique corresponds exactly to the set of core nodes. Figure 4.1
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