Graphs have attracted research efforts for about three centuries (Biggs et al. 1986). Earlier, the focus was mainly on mathematical models and algorithms. Graph theory continues to be an active research area that includes many open problems. In the last decade, there has been a considerable amount of research in analyzing and processing large graph structures for real applications. This effort has led to the development of several alternative algorithms, techniques, and big data systems.
Parallel systems are very important and big data challenges are relevant to graph analytics. Although the edge list of some existing graphs are relatively small in size and may fit in one machine, these graphs are often large when considering vertex properties, graph indexes, and intermediate results during query processing. Processing often leads to very large memory requirements, which makes it not practical to fit the entire graph and its data into a single machine.
Big graph data has a...
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