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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.