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Over the last decade, the vertex-centric programming model has attracted significant attention in the world of graph processing, resulting in the emergence of a number of vertex-centric frameworks. Its simple programming interface, where computation is expressed from a vertex point of view, offers both ease of programming to the user and inherent parallelism for the underlying framework to leverage. However, vertex-centric programs represent an extreme form of irregularity, both inter and intra core. This is because they exhibit a variety of challenges from a workload that may greatly vary across supersteps, through fine-grain synchronisations, to memory accesses that are unpredictable both in terms of quantity and location. In this paper, we explore three optimisations which address these irregular challenges; a hybrid combiner carefully coupling lock-free and lock-based combinations, the partial externalisation of vertex structures to improve locality and the shift to an edge-centric representation of the workload. The optimisations were integrated into the iPregel vertex-centric framework, enabling the evaluation of each optimisation in the context of graph processing across three general purpose benchmarks common in the vertex-centric community, each run on four publicly available graphs covering all orders of magnitude from a million to a billion edges. The result of this work is a set of techniques which we believe not only provide a significant performance improvement in vertex-centric graph processing, but are also applicable more generally to irregular applications.