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Graphs are widely used to model real world objects and their relationships, and large graph datasets are common in many application domains. To understand the underlying characteristics of large graphs, graph summarization techniques are critical. However, existing graph summarization methods are mostly statistical (Studying statistics such as degree distributions, hop-plots and clustering coefficients). These statistical methods are very useful, but the resolutions of the summaries are hard to control. In this paper, the authors introduce two database-style operations to summarize graphs. Like the OLAP-style aggregation methods that allow users to drill-down or roll-up to control the resolution of summarization, the methods provide an analogous functionality for large graph datasets.
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