Name
Abstract | ||
|---|---|---|
Information is a valuable asset in socio-economic systems, a significant part of which is entailed into the 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 contagion in a network of liabilities), agents capable of providing a better estimation of (otherwise) inaccessible network properties, ultimately have a competitive advantage. In this paper, we address the issue of quantifying the information asymmetry of nodes: to this aim, we define a novel index—InfoRank—intended to rank nodes according to their information content. In order to do so, each node ego-network is enforced as a constraint of an entropy-maximization problem and the subsequent uncertainty reduction is used to quantify the node-specific accessible information. We, then, test the performance of our ranking procedure in terms of reconstruction accuracy and show that it outperforms other centrality measures in identifying the “most informative” nodes. Finally, we discuss the socio-economic implications of network information asymmetry. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s10955-018-2076-z | Journal of Statistical Physics |
Keywords | Field | DocType |
Complex networks,Shannon entropy,Information theory,Ranking algorithm | Information theory,Data mining,Information asymmetry,Ranking,Competitive advantage,Centrality,Complex network,Artificial intelligence,Entropy (information theory),Mathematics,Machine learning,Uncertainty reduction theory | Journal |
Volume | Issue | ISSN |
abs/1710.09656 | 3-4 | J. Stat. Phys. (2018) |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paolo Barucca | 1 | 11 | 3.76 |
| Guido Caldarelli | 2 | 382 | 40.76 |
| Tiziano Squartini | 3 | 67 | 11.86 |