NScale: Neighborhood-Centric Large-Scale Graph Analytics in the Cloud

Provided by: Cornell University
Topic: Cloud
Format: PDF
There is an increasing interest in executing rich and complex analysis tasks over large-scale graphs, many of which require processing and reasoning about a large number of multi-hop neighborhoods or sub-graphs in the graph. Examples of such tasks include ego network analysis, motif counting, finding social circles, personalized recommendations, link prediction, anomaly detection, analyzing influence cascades, and so on. These tasks are not well served by the existing vertex-centric graph processing frameworks, whose computation and execution models limit the user program to directly access the state of a single vertex; these results in high communication, scheduling, and memory overheads in executing such tasks using those frameworks.

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