NScale: Neighborhood-centric Large-Scale Graph Analytics in the Cloud
Abdul Quamar, Amol Deshpande, Jimmy Lin

TL;DR
NSCALE is a new cloud-based graph analytics framework that allows subgraph-centric processing, enabling complex neighborhood analyses with significant performance and cost improvements over traditional vertex-centric methods.
Contribution
Introduces NSCALE, a subgraph-centric graph processing framework with a novel graph extraction and packing module, improving large-scale graph analytics efficiency.
Findings
Orders-of-magnitude performance improvements
Drastic reductions in analytics costs
Efficient subgraph extraction and packing
Abstract
There is an increasing interest in executing complex analyses over large graphs, many of which require processing a large number of multi-hop neighborhoods or subgraphs. Examples include ego network analysis, motif counting, personalized recommendations, and others. These tasks are not well served by existing vertex-centric graph processing frameworks, where user programs are only able to directly access the state of a single vertex. This paper introduces NSCALE, a novel end-to-end graph processing framework that enables the distributed execution of complex subgraph-centric analytics over large-scale graphs in the cloud. NSCALE enables users to write programs at the level of subgraphs rather than at the level of vertices. Unlike most previous graph processing frameworks, which apply the user program to the entire graph, NSCALE allows users to declaratively specify subgraphs of interest.…
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Taxonomy
TopicsGraph Theory and Algorithms · Advanced Graph Neural Networks · Cloud Computing and Resource Management
