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An Empirical Comparison of Big Graph Frameworks in the Context of Network Analysis
[article]
2016
arXiv
pre-print
Complex networks are relational data sets commonly represented as graphs. The analysis of their intricate structure is relevant to many areas of science and commerce, and data sets may reach sizes that require distributed storage and processing. We describe and compare programming models for distributed computing with a focus on graph algorithms for large-scale complex network analysis. Four frameworks - GraphLab, Apache Giraph, Giraph++ and Apache Flink - are used to implement algorithms for
arXiv:1601.00289v1
fatcat:zr3icqvs2fa4vnmjgh2il25e3e