Evaluation of Communities from Exploratory Evolutionary Compression of Weighted Graphs

Contact networks are used as a representation for the modeling of illness transmission. In this study, we represent not only the links of the transmissions but also utilize a weighted graph to represent the probability of transfer. These graphs can be large and complex when taking into account the number of contacts used in tracing. Compression of the graph allows for the development of community detection as well as providing a simpler graph. By examining the contact networks developed by an evolutionary algorithm for compression, it is discovered that the choice of fitness function and the appropriate weighting of edges leads to a different compressed graph, finding different connected communities; this is also true when compared to the communities identified by the Louvain community detection algorithm. This demonstrates the importance of considering weighting in contact networks, and suggests that in the future an understanding of the community structure should be utilized by public health officials.