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Title: G-thinker: A Distributed Framework for Mining Subgraphs in a Big Graph
Mining from a big graph those subgraphs that satisfy certain conditions is useful in many applications such as community detection and subgraph matching. These problems have a high time complexity, but existing systems to scale them are all IO-bound in execution. We propose the first truly CPU-bound distributed framework called G-thinker that adopts a user-friendly subgraph-centric vertex-pulling API for writing distributed subgraph mining algorithms. To utilize all CPU cores of a cluster, G-thinker features (1) a highly-concurrent vertex cache for parallel task access and (2) a lightweight task scheduling approach that ensures high task throughput. These designs well overlap communication with computation to minimize the CPU idle time. Extensive experiments demonstrate that G-thinker achieves orders of magnitude speedup compared even with the fastest existing subgraph-centric system, and it scales well to much larger and denser real network data. G-thinker is open-sourced at http://bit.ly/gthinker with detailed documentation.  more » « less
Award ID(s):
1755464
NSF-PAR ID:
10140007
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
Proceedings of the 36th IEEE International Conference on Data Engineering (ICDE)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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