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Structural Health Monitoring (SHM), is the process of monitoring a system or structure with the objective of utilising the acquired data to assess the overall condition (the health) of the object in question. One problem of such an approach is acquiring enough data to characterise the `health' states of a structure. Population-based Structural Health Monitoring (PBSHM) is a recent development within the SHM community which attempts to bypass the data bottlenecks present in the `classic' SHM scenario. The aim of PBSHM is that by monitoring multiple structures (the population) one can gain additional insights into the health of a particular structure when using population data, compared to the insights available when using only a single structure's data. PBSHM operates under the premise that learnt knowledge may be shared across structures in the population; however, before any knowledge is shared, a similarity between structures is first established, to guide if an attempt to share knowledge should occur. The work presented encompasses the similarity generation process of PBSHM; each structure is first described through a standardised language (an Irreducible Element model), a graph representation of each structure is placed within a network, and finally links are generated between the structures dependent upon the derived similarity from comparisons algorithms. The presented work will include the current state of play with similarity comparisons within PBSHM and how the network is being adapted to deal with problems such as author bias.