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There are a wide number of graph centrality metrics. Further, the performance of each can vary widely depending on the type of implementation. In this work we present our implementation of triangle centrality in Arkouda with several different triangle counting methods. Triangle Centrality is a robust metric that captures the centrality of a vertex through both a vertex’s own connectedness and that of its neighbors. Arkouda is an open-source framework for data science at the scale of terabytes and beyond. These methods are compared against each other and another shared memory implementation. 

Due to the emergence of massive real-world graphs, whose sizes may extend to terabytes, new tools must be developed to enable data scientists to handle such graphs efficiently. These graphs may include social networks, computer networks, and genomes. In this paper, we propose a novel graph package, Arachne, to make large-scale graph analytics more effortless and efficient based on the open-source Arkouda framework. Arkouda has been developed to allow users to perform massively parallel computations on distributed data with an interface similar to NumPy. In this package, we developed a fundamental sparse graph data structure and then built several useful graph algorithms around our data structure to form a basic algorithmic library. Benchmarks and tools were also developed to evaluate and demonstrate the use of our graph algorithms. The graph algorithms we have implemented thus far include breadth-first search (BFS), connected components (CC), k-Truss (KT), Jaccard coefficients (JC), triangle counting (TC), and triangle centrality (TCE). Their corresponding experimental results based on realworld and synthetic graphs are presented. Arachne is organized as an Arkouda extension package and is publicly available on GitHub (https://github.com/Bears-R-Us/arkouda-njit).