A14: Analysis of Synthetic Graph Generation Methods for Directed Network Graphs
Student: Spencer Callicott (Mississippi State University)
Supervisor: Stefano Iannucci (Distributed Analytics and Security Institute, Mississippi State University)
Abstract: Historically, scientific experiments have been conducted to generate scale-free network graphs based on structure. Metrics used to measure veracity ensure the integrity of a scale-free algorithm given a seed. However, studies do not explore the performance benefits or drawbacks of specific algorithms running on Apache Spark and GraphX. Recognizing the lack of performance benchmarks demands ensuring accuracy through experimenting. This study will utilize the Stochastic Kronecker Graph model to synthetically generate graphs given a seed graph.
ACM-SRC Semi-Finalist: no
Poster: pdf
Two-page extended abstract: pdf
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