Graph Scrutinizer

Graph-Scrutinizer offers comprehensive capabilities for scalable graph processing, employing graph sampling, partitioning, and summarization to handle large-scale graphs efficiently within single computer systems. It introduces advanced functionalities, including heuristics and neural query execution engines, interoperability between time series and graphs, and generative capabilities, targeting a wide range of use cases.

Graph-Scrutinizer’s design aims to avoid processing entire massive-scale graphs by obtaining relevant samples, thus addressing the challenges of analyzing and mining large-scale graph data, as well as balancing execution time, exactness of results, and energy efficiency. This tool will be integrated with other tools under the Graph-Massivizer project to support efficient and environmentally friendly graph processing across diverse environments, including cloud, edge, and HPC.