Efficiency and Precision Trade-Offs in Graph Summary Algorithms
Refereed Conference Meeting Proceeding
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.
International Database Engineering & Applications Symposium
ACM Digital Library
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National University of Ireland, Galway (NUIG)
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