Equilibrium statistical mechanics on correlated random graphs
Adriano Barra, Elena Agliari

TL;DR
This paper introduces a modified Hopfield model where varying pattern biases induce different network topologies, including small-world properties, and analytically explores the resulting thermodynamics and critical behavior.
Contribution
It proposes a novel approach to generate correlated random graphs with emergent topologies by shifting pattern definitions in a Hopfield model, avoiding replicas and using sub-graph magnetizations.
Findings
Network topology varies with pattern bias, including fully connected and diluted regimes.
Thermodynamics is analytically solved at replica symmetric level.
Dilution reduces coupling strength but preserves phase types.
Abstract
Biological and social networks have recently attracted enormous attention between physicists. Among several, two main aspects may be stressed: A non trivial topology of the graph describing the mutual interactions between agents exists and/or, typically, such interactions are essentially (weighted) imitative. Despite such aspects are widely accepted and empirically confirmed, the schemes currently exploited in order to generate the expected topology are based on a-priori assumptions and in most cases still implement constant intensities for links. Here we propose a simple shift in the definition of patterns in an Hopfield model to convert frustration into dilution: By varying the bias of the pattern distribution, the network topology -which is generated by the reciprocal affinities among agents - crosses various well known regimes (fully connected, linearly diverging connectivity,…
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