Network reconstruction via density sampling

Abstract

Reconstructing weighted networks from partial information is necessary in many important circumstances, e.g. for a correct estimation of systemic risk. It has been shown that, in order to achieve an accurate reconstruction, it is crucial to reliably replicate the empirical degree sequence, which is however unknown in many realistic situations. More recently, it has been found that the knowledge of the degree sequence can be replaced by the knowledge of the strength sequence, which is typically accessible, complemented by that of the total number of links, thus considerably relaxing the observational requirements. Here we further relax these requirements and devise a procedure valid when even the the total number of links is unavailable. We assume that, apart from the heterogeneity induced by the degree sequence itself, the network is homogeneous, so that its (global) link density can be estimated by sampling subsets of nodes with representative density. We show that the best way of sampling nodes is the random selection scheme, any other procedure being biased towards unrealistically large, or small, link densities. We then introduce our core technique for reconstructing both the topology and the link weights of the unknown network in detail. When tested on real economic and financial data sets, our method achieves a remarkable accuracy and is very robust with respect to the sampled subsets, thus representing a reliable practical tool whenever the available topological information is restricted to small portions of nodes.

8 pages, 1 figure, 4 tables

References in corpus (7)

• A fitness model for the Italian Interbank Money Market
• Systemic risk analysis in reconstructed economic and financial networks
• Enhanced reconstruction of weighted networks from strengths and degrees
• Bootstrapping topology and systemic risk of complex network using the fitness model
• Compensating for population sampling in simulations of epidemic spread on temporal contact networks
• Estimating topological properties of weighted networks from limited information
• Empirical comparison of network sampling techniques

Cited by in corpus (13)

• The Statistical Physics of Real-World Networks
• Nestedness in complex networks: Observation, emergence, and implications
• Network Valuation in Financial Systems
• Reconstruction methods for networks: the case of economic and financial systems
• Enhanced capital-asset pricing model for the reconstruction of bipartite financial networks
• Reconstructing networks
• A faster horse on a safer trail: generalized inference for the efficient reconstruction of weighted networks
• Systemic liquidity contagion in the European interbank market
• Irreducible network backbones: unbiased graph filtering via maximum entropy
• Estimating the epidemic risk using non-uniformly sampled contact data
• Tackling information asymmetry in networks: a new entropy-based ranking index
• Critical density for network reconstruction
• Reconstruction of multiplex networks with correlated layers