(self, *args, **kwds)
return VertexClustering(self, membership, modularity_params=modularity_params)
Finds the community structure of the graph according to the
        spinglass community detection method of Reichardt & Bornholdt.

        @param weights: edge weights to be used. Can be a sequence or
          iterable or even an edge attribute name.
        @param spins: integer, the number of spins to use. This is the
          upper limit for the number of communities. It is not a problem
          to supply a (reasonably) big number here, in which case some
          spin states will be unpopulated.
        @param parupdate: whether to update the spins of the vertices in
          parallel (synchronously) or not
        @param start_temp: the starting temperature
        @param stop_temp: the stop temperature
        @param cool_fact: cooling factor for the simulated annealing
        @param update_rule: specifies the null model of the simulation.
          Possible values are C{"config"} (a random graph with the same
          vertex degrees as the input graph) or C{"simple"} (a random
          graph with the same number of edges)
        @param gamma: the gamma argument of the algorithm, specifying the
          balance between the importance of present and missing edges
          within a community. The default value of 1.0 assigns equal
          importance to both of them.
        @param implementation: currently igraph contains two implementations
          of the spinglass community detection algorithm. The faster
          original implementation is the default. The other implementation
          is able to take into account negative weights, this can be
          chosen by setting C{implementation} to C{"neg"}
        @param lambda_: the lambda argument of the algorithm, which
          specifies the balance between the importance of present and missing
          negatively weighted edges within a community. Smaller values of
          lambda lead to communities with less negative intra-connectivity.
          If the argument is zero, the algorithm reduces to a graph coloring
          algorithm, using the number of spins as colors. This argument is
          ignored if the original implementation is used. Note the underscore
          at the end of the argument name; this is due to the fact that
          lambda is a reserved keyword in Python.
        @return: an appropriate L{VertexClustering} object.

        @newfield ref: Reference
        @ref: Reichardt J and Bornholdt S: Statistical mechanics of
          community detection. Phys Rev E 74:016110 (2006).
          U{http://arxiv.org/abs/cond-mat/0603718}.
        @ref: Traag VA and Bruggeman J: Community detection in networks
          with positive and negative links. Phys Rev E 80:036115 (2009).
          U{http://arxiv.org/abs/0811.2329}.
Finds the community structure of the graph according to the
        spinglass community detection method of Reichardt & Bornholdt.
Finds the community structure of the graph according to the spinglass community detection method of Reichardt & Bornholdt .
def community_spinglass(self, *args, **kwds):
        """Finds the community structure of the graph according to the
        spinglass community detection method of Reichardt & Bornholdt.

        @param weights: edge weights to be used. Can be a sequence or
          iterable or even an edge attribute name.
        @param spins: integer, the number of spins to use. This is the
          upper limit for the number of communities. It is not a problem
          to supply a (reasonably) big number here, in which case some
          spin states will be unpopulated.
        @param parupdate: whether to update the spins of the vertices in
          parallel (synchronously) or not
        @param start_temp: the starting temperature
        @param stop_temp: the stop temperature
        @param cool_fact: cooling factor for the simulated annealing
        @param update_rule: specifies the null model of the simulation.
          Possible values are C{"config"} (a random graph with the same
          vertex degrees as the input graph) or C{"simple"} (a random
          graph with the same number of edges)
        @param gamma: the gamma argument of the algorithm, specifying the
          balance between the importance of present and missing edges
          within a community. The default value of 1.0 assigns equal
          importance to both of them.
        @param implementation: currently igraph contains two implementations
          of the spinglass community detection algorithm. The faster
          original implementation is the default. The other implementation
          is able to take into account negative weights, this can be
          chosen by setting C{implementation} to C{"neg"}
        @param lambda_: the lambda argument of the algorithm, which
          specifies the balance between the importance of present and missing
          negatively weighted edges within a community. Smaller values of
          lambda lead to communities with less negative intra-connectivity.
          If the argument is zero, the algorithm reduces to a graph coloring
          algorithm, using the number of spins as colors. This argument is
          ignored if the original implementation is used. Note the underscore
          at the end of the argument name; this is due to the fact that
          lambda is a reserved keyword in Python.
        @return: an appropriate L{VertexClustering} object.

        @newfield ref: Reference
        @ref: Reichardt J and Bornholdt S: Statistical mechanics of
          community detection. Phys Rev E 74:016110 (2006).
          U{http://arxiv.org/abs/cond-mat/0603718}.
        @ref: Traag VA and Bruggeman J: Community detection in networks
          with positive and negative links. Phys Rev E 80:036115 (2009).
          U{http://arxiv.org/abs/0811.2329}.
        """
        membership = GraphBase.community_spinglass(self, *args, **kwds)
        if "weights" in kwds:
            modularity_params = dict(weights=kwds["weights"])
        else:
            modularity_params = {}
        return VertexClustering(self, membership, modularity_params=modularity_params)
def community_spinglass ( self , * args , * * kwds ) : membership = GraphBase . community_spinglass ( self , * args , * * kwds ) if "weights" in kwds : modularity_params = dict ( weights = kwds [ "weights" ] ) else : modularity_params = { } return VertexClustering ( self , membership , modularity_params = modularity_params )