Title
Synthetic Graph Generation for Systematic Exploration of Graph Structural Properties.
Abstract
High performance graph processing poses significant challenges for both algorithm and platform designers due to the large performance variability it exhibits: performance depends on the algorithm, the dataset, and the (hardware/software) platform. Traditionally, performance variability is tackled by extensive benchmarking, modeling, and, eventually, better or smarter algorithms. Our own research into the impact of datasets on the performance of graph algorithms has convinced us that such extensive benchmarking is very difficult for graph processing simply because we lack input data: the public datasets and graph generation tools currently available are insufficient for a systematic investigation of the impact of different graph properties. In this work we propose to alleviate this problem by using evolutionary computing as a method to generate graphs. Our goal is allow users to request graphs with a given set of properties (e.g., number of vertices, number of edges, degree distribution), and enable the generator to evolve graphs until they satisfy the request. Such a fine-grain, flexible exploration of graphs and their properties will finally enable a statistical take on performance analysis and modeling for graph processing. To this end, our work-in-progress paper presents the design of our generator, discusses the challenges and trade-offs to be encountered, and reveals our preliminary results.
Year
DOI
Venue
2016
10.1007/978-3-319-58943-5_45
Lecture Notes in Computer Science
Field
DocType
Volume
Graph property,Computer science,Theoretical computer science,Software,Degree distribution,Artificial intelligence,Benchmarking,Distributed computing,Graph,Vertex (geometry),Evolutionary computation,Graph (abstract data type),Machine learning
Conference
10104
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Merijn Verstraaten1564.96
Ana Lucia Varbanescu252044.83
Cees de Laat31138149.30