Abstract
Graph processing is becoming ubiquitous due to the proliferation of interconnected data in several domains, including life sciences, social networks, cybersecurity, finance and logistics, to name a few. In parallel with the growth of the underlying graph data sources, a plethora of graph workloads have appeared, ranging from graph analytics to graph traversals and graph pattern matching. Graph systems executing both complex and simple graph workloads need to leverage adequate data structures for efficiently processing heterogeneous graph data. While the underlying graph data structures have been extensively studied for the static case, they are less understood for the dynamic case, with the data undergoing several updates per second. Moreover, the existing solutions suffer lack of generality, as they focus on one specific requirement and workload type at a time. Designing a universal data structure that adapts to several kinds of graph workloads in a dynamic setting and achieves significant efficiency on all of them is far from being trivial.
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