Graphs or networks provide a powerful abstraction to view and analyze relationships among different entities present in a dataset. However, much of the data of interest to analysts and data scientists resides in non-graph forms such as relational databases, JSON, XML, CSV and text. The effort and skill required in identifying and extracting the relevant graph representation from data is often the prohibitive and limits a wider adoption of graph-based analysis of nongraph data. In this paper, we

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... demonstrate our system called GraphViewer, for accelerated graph-based exploration and analysis. It automatically discovers relevant graphs implicit within a given non-graph dataset using a set of novel rulebased and data-driven techniques, and optimizes their extraction and storage. It computes several node and graph level metrics and detects anomalous entities in data. Finally, it summarizes the results to support interpretation by a human analyst. While the system automates the computationally intensive aspects of the process, it is engineered to leverage human domain expertise and instincts to fine tune the data exploration process.