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Efficiency and Precision Trade-Offs in Graph Summary Algorithms

Publication Type: 
Refereed Conference Meeting Proceeding
Abstract: 
In many applications, it is convenient to substitute a large data graph with a smaller homomorphic graph. Accurate graph summarization algorithms are sub-optimal for a shared-nothing infrastructure such as MapReduce as they require multiple iterations over the data graph. We investigate approximate graph summarization algorithms that are efficient to compute in a shared-nothing infrastructure. We evaluate over several datasets the trade-offs between performance and precision. Experiments highlight the need of trade-offs between precision and complexity of a summarization algorithm.
Conference Name: 
International Database Engineering & Applications Symposium
Proceedings: 
ACM Digital Library
Digital Object Identifer (DOI): 
10.1145/2513591.2513654
Publication Date: 
09/10/2013
Conference Location: 
Spain
Research Group: 
Institution: 
National University of Ireland, Galway (NUIG)
Open access repository: 
Yes
Publication document: