ModularityBP Object¶
We have created a single Python object, modbp.ModularityBP
 Store graphs on ModularityBP Object
 Run modularity belief propagation
 Access discovered partitions and marginals
 Compile results across many different runs
Storing Graphs on ModularityBP Object¶
A representation of a network must be supplied at the instantiation of the modbp.ModularityBP
object. Each instance can only be associated with one network. A network can be supplied in several different formats:
A
igraph.Graph
object is supplied. In this case network will be treated as single layer. This is pass in through the mlgraph parameter:rand_g=igraph.Graph.ErdosRenyi(n=100,p=.05) modbp_obj=modbp.ModularityBP(mlgraph=rand_g)
A
modbp.MultilayerGraph
object can be supplied, also through the mlgraph parameter. See Multilayer Graph for more details.Finally, one can pass in an array for intralayer_edges, interlayer_edges, and layer_vec, from which
modbp.ModularityBP
will internally construct amodbp.MultilayerGraph
:intra_edges=np.array([[0,1],[0,2],[3,4]]) inter_edges=np.array([[2,3]]) layer_vec=[0,0,0,1,1] modbp_obj=modbp.ModularityBP(intra_edges=intra_edges, inter_edges=inter_edges, layer_vec=layer_vec)
The graph is stored internally each modbp.ModularityBP
as a modbp.MultilayerGraph
and is accessible through modbp.ModularityBP.graph
variable.
Running Multilayer Modularity Belief Propagation¶

ModularityBP.
run_modbp
(beta, q, niter=100, resgamma=1.0, omega=1.0, reset=False)[source]¶ Parameters:  beta – The inverse tempature parameter at which to run the modularity belief propagation algorithm. Must be specified each time BP is run.
 q – The number of mariginals used for the run of modbp. Note that if self.use_effective is true, The final number of reported communities could be lower.
 niter – Maximum number of iterations allowed. If self._align_communities_across_layers is true, the actual number of runs could be higher than this upper bound though at most 2*niter
 resgamma – The resolution parameter at which to run modbp. Default is resgamma=1.0
 omega – The coupling strength used in running multimodbp. This represent how strongly the algorithm tries to assign nodes connected by an interlayer connection to the same community.
 reset – If true, the marginals will be rerandomized when this method is called. Otherwise the state will be maintained from previous runs if existing (assuming q hasn’t changed).
Returns: None