Speaker
Dr
Juyong Park
(Kyung Hee University)
Description
Exponential random graph theory is the complex network
analog of the canonical ensemble theory from statistical
physics. While it has been particularly successful in
modeling networks with specified degree distributions, a
naïve model of a clustered network using a graph Hamiltonian
linear in the number of triangles has been shown to undergo
an abrupt transition into an unrealistic phase of extreme
clustering via triangle condensation. Here we study a
non-linear graph Hamiltonian that explicitly forbids such a
condensation and show numerically that it generates an
equilibrium phase with specified intermediate clustering. We
also discuss some applications based on Hamiltonian-based
graph theory.
