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As graph representations of data emerge in multiple domains, data analystsneed to be able to intelligently select among a magnitude of different datagraphs based on the effects different graph operators have on them. Exhaustiveexecution of an operator over the bulk of available data sources is impracticaldue to the massive resources it requires. Additionally, the same process wouldhave to be re-implemented whenever a different operator is considered. Toaddress this challenge, this work proposes an efficient graph operator modelingmethodology. Our novel approach focuses on the inputs themselves, utilizinggraph similarity to infer knowledge about input graphs. The modeled operator isonly executed for a small subset of the available graphs and its behavior isapproximated for the rest of the graphs using machine learning techniques. Ourmethod is operator-agnostic, as the same similarity information can be reusedfor modeling multiple graph operators. We also propose a family of similaritymeasures based on the degree distribution that prove capable of producing highquality estimations, comparable or even surpassing other much more costly,state-of-the-art similarity measures. Our evaluation over both real-world andsynthetic graphs indicates that our method achieves extremely accurate modelingof many commonly encountered operators, managing massive speedups over abrute-force alternative.

Graph Operator Modeling over Large Graph Datasets