  Graph algorithms enormously contribute to the domains such as blockchains,
social networks, biological networks, telecommunication networks, and several
others. The ever-increasing demand of data-volume, as well as speed of such
applications, have essentially transported these applications from their
comfort zone: static setting, to a challenging territory of dynamic updates. At
the same time, mainstreaming of multi-core processors have entailed that the
dynamic applications should be able to exploit concurrency as soon as
parallelization gets inhibited. Thus, the design and implementation of
efficient concurrent dynamic graph algorithms have become significant.
  This paper reports a novel library of concurrent shared-memory algorithms for
breadth-first search (BFS), single-source shortest-path (SSSP), and betweenness
centrality (BC) in a dynamic graph. The presented algorithms are provably
non-blocking and linearizable. We extensively evaluate C++ implementations of
the algorithms through several micro-benchmarks. The experimental results
demonstrate the scalability with the number of threads. Our experiments also
highlight the limitations of static graph analytics methods in a dynamic
setting.
