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Analytical graph algorithms commonly compute metrics for a graph at one point in time. In practice it is often also of interest how metrics change over time, e.g., to find trends. For this purpose, algorithms must be executed for multiple graph snapshots. We present Single Algorithm Multiple Snapshots (SAMS) , a novel approach to execute algorithms concurrently for multiple graph snapshots. SAMS automatically transforms graph algorithms to leverage similarities between the analyzed graph snapshots. The automatic transformation interleaves algorithm executions on multiple snapshots, synergistically shares their graph accesses and traversals, and optimizes the algorithm's data layout. Thus, SAMS can amortize the cost of random data accesses and improve memory bandwidth utilization---two main cost factors in graph analytics. We extensively evaluate SAMS using six well-known algorithms and multiple synthetic as well as real-world graph datasets. Our measurements show that in multi-snapshot analyses, SAMS offers runtime improvements of up to two orders of magnitude over traditional snapshot-at-a-time execution.

Graph analytics on social networks, Web data, and communication networks has been widely used in a plethora of applications. Many graph analytics algorithms are based on breadth-first search (BFS) graph traversal, which is not only time-consuming for large datasets but also involves much redundant computation when executed multiple times from different start vertices. In this paper, we propose Multi-Source BFS (MS-BFS), an algorithm that is designed to run multiple concurrent BFSs over the same graph on a single CPU core while scaling up as the number of cores increases. MS-BFS leverages the properties of small-world networks, which apply to many real-world graphs, and enables efficient graph traversal that: (i) shares common computation across concurrent BFSs; (ii) greatly reduces the number of random memory accesses; and (iii) does not incur synchronization costs. We demonstrate how a real graph analytics application-all-vertices closeness centrality-can be efficiently solved with MS-BFS. Furthermore, we present an extensive experimental evaluation with both synthetic and real datasets, including Twitter and Wikipedia, showing that MS-BFS provides almost linear scalability with respect to the number of cores and excellent scalability for increasing graph sizes, outperforming state-of-the-art BFS algorithms by more than one order of magnitude when running a large number of BFSs.