  The polymer model framework is a classical tool from statistical mechanics
that has recently been used to obtain approximation algorithms for spin systems
on classes of bounded-degree graphs; examples include the ferromagnetic Potts
model on expanders and on the grid. One of the key ingredients in the analysis
of polymer models is controlling the growth rate of the number of polymers,
which has been typically achieved so far by invoking the bounded-degree
assumption. Nevertheless, this assumption is often restrictive and obstructs
the applicability of the method to more general graphs. For example, sparse
random graphs typically have bounded average degree and good expansion
properties, but they include vertices with unbounded degree, and therefore are
excluded from the current polymer-model framework.
  We develop a less restrictive framework for polymer models that relaxes the
standard bounded-degree assumption, by reworking the relevant polymer models
from the edge perspective. The edge perspective allows us to bound the growth
rate of the number of polymers in terms of the total degree of polymers, which
in turn can be related more easily to the expansion properties of the
underlying graph. To apply our methods, we consider random graphs with
unbounded degrees from a fixed degree sequence (with minimum degree at least 3)
and obtain approximation algorithms for the ferromagnetic Potts model, which is
a standard benchmark for polymer models. Our techniques also extend to more
general spin systems.
