Information is a valuable asset for agents in socio-economic systems, a
significant part of the information being entailed into the very network of
connections between agents. The different interlinkages patterns that agents
establish may, in fact, lead to asymmetries in the knowledge of the network
structure; since this entails a different ability of quantifying relevant
systemic properties (e.g. the risk of financial contagion in a network of
liabilities), agents capable of providing a better estimate of (otherwise)
unaccessible network properties, ultimately have a competitive advantage. In
this paper, we address for the first time the issue of quantifying the
information asymmetry arising from the network topology. To this aim, we define
a novel index - InfoRank - intended to measure the quality of the information
possessed by each node, computing the Shannon entropy of the ensemble
conditioned on the node-specific information. Further, we test the performance
of our novel ranking procedure in terms of the reconstruction accuracy of the
(unaccessible) network structure and show that it outperforms other popular
centrality measures in identifying the "most informative" nodes. Finally, we
discuss the socio-economic implications of network information asymmetry.Comment: 12 pages, 8 figure
