A Similarity-based Approach to Modeling Graph Operators

As graph representations of data emerge in multiple domains, data analysts need to be able to select among a magnitude of di‚erent graph inputs based on the effects they induce when a variety of operators are applied to them. Exhaustive execution of an operator over the bulk of the available data sources is impractical due to the massive resources it requires. Additionally, the same process would have to be re-implemented whenever a di‚erent operator is considered. To address this challenge, this work proposes an ecient graph operator modeling methodology. Our novel approach focuses on the inputs themselves, utilizing graph similarity to infer knowledge about multiple input graphs. Œe modeled operator is only executed for a small subset of the available graphs and its behavior is approximated for the rest of the graphs using Machine Learning. Œis method is operator-agnostic, as the same similarity information can be reused for modeling multiple graph operators. We also propose a family of similarity metrics based on the degree distribution that prove capable of producing high quality estimations, comparable or even surpassing a much more costly, state-of-the-art similarity metric. Our evaluation over both real-world and synthetic graphs indicates that our method achieves extremely accurate modeling of many commonly encountered operators, managing massive speedups over a brute-force alternative.