The exchange of genetic information between coral reefs through the transport of larvae can be described in terms of networks that capture the linkages between distant populations. A key question arising from these networks is the determination of the highly connected modules (communities). Communities can be defined using genetic similarity or distance statistics between multiple samples but due to limited specimen sampling capacity the boundaries of the communities for the known coral reefs in the seascape remain unresolved. In this study we use the microsatellite composition of individual corals to compare sample populations using a genetic dissimilarity measure (FST) which is then used to create a complex network. This network involved sampling 1025 colonies from 22 collection sites and examining 10 microsatellites loci. The links between each sampling site were given a strength that was created from the pair wise FST values. The result is an undirected weighted network describing the genetic dissimilarity between each sampled population. From this network we then determined the community structure using a leading eigenvector algorithm within graph theory. However, given the relatively limited sampling conducted, the representation of the regional genetic structure was incomplete. To assist with defining the boundaries of the genetically based communities we also integrated the communities derived from a hydrodynamic and distance based networks. The hydrodynamic network, though more comprehensive, was of smaller spatial extent than our genetic sampling. A Bayesian Belief network was developed to integrate the overlapping communities. The results indicate the genetic population structure of the Great Barrier Reef and provide guidance on where future genetic sampling should take place to complete the genetic diversity mapping.