A Framework to Quantify Approximate Simulation on Graph Data

October 18, 2020 ยท The Ethereal ยท ๐Ÿ› IEEE International Conference on Data Engineering

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Authors Xiaoshuang Chen, Longbin Lai, Lu Qin, Xuemin Lin, Boge Liu arXiv ID 2010.08938 Category cs.LO: Logic in CS Cross-listed cs.DB Citations 7 Venue IEEE International Conference on Data Engineering Last Checked 1 month ago
Abstract
Simulation and its variants (e.g., bisimulation and degree-preserving simulation) are useful in a wide spectrum of applications. However, all simulation variants are coarse "yes-or-no" indicators that simply confirm or refute whether one node simulates another, which limits the scope and power of their utility. Therefore, it is meaningful to develop a fractional $ฯ‡$-simulation measure to quantify the degree to which one node simulates another by the simulation variant $ฯ‡$. To this end, we first present several properties necessary for a fractional $ฯ‡$-simulation measure. Then, we present $FSim_ฯ‡$, a general fractional $ฯ‡$-simulation computation framework that can be configured to quantify the extent of all $ฯ‡$-simulations. Comprehensive experiments and real-world case studies show the measure to be effective and the computation framework to be efficient.
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