An Analysis on Graph-Processing Frameworks: Neo4j and Spark GraphX - Artificial Intelligence Applications and Innovations Access content directly
Conference Papers Year : 2022

An Analysis on Graph-Processing Frameworks: Neo4j and Spark GraphX

Abstract

Numerous graph algorithms have been developed to address a variety of problems in the industry, ranging from fraud detection to scheduling or even recommendation systems. Graph-processing frameworks are hence created to simplify the implementation of graph-based solutions. Nonetheless, the number of such frameworks has grown significantly over the past decades with varying benefits and drawbacks. Understanding the requirements and characteristics of each framework plays a vital role in the selection of a suitable solution to a given problem. In this work, we evaluate the performance and usability of 2 popular graph-processing frameworks Neo4j and Apache Spark GraphX by implementing a PageRank solution to solve a practical business problem derived from the Yelp dataset.
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hal-04317173 , version 1 (01-12-2023)

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Alabbas Alhaj Ali, Doina Logofătu. An Analysis on Graph-Processing Frameworks: Neo4j and Spark GraphX. 18th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2022, Hersonissos, Greece. pp.461-470, ⟨10.1007/978-3-031-08333-4_37⟩. ⟨hal-04317173⟩
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