Paper Info
Title
G-thinker: Big Graph Mining Made Easier and Faster.
Abstract
This paper proposes a general system for compute-intensive graph mining tasks that find from a big graph all subgraphs that satisfy certain requirements (e.g., graph matching and community detection). Due to the broad range of applications of such tasks, many single-threaded algorithms have been proposed. However, graphs such as online social networks and knowledge graphs often have billions of vertices and edges, which require distributed processing in order to scale. Unfortunately, existing distributed graph processing systems such as Pregel and GraphLab are designed for data-intensive analytics, and are inefficient for compute-intensive graph mining tasks since computation over any data is coupled with the datau0027s access that involves network transmission. We propose a distributed graph mining framework, called G-thinker, which is designed for compute-intensive graph mining workloads. G-thinker provides an intuitive graph-exploration API for the convenient implementation of various graph mining algorithms, and the runtime engine provides efficient execution with bounded memory consumption, light network communication, and parallelism between computation and communication. Extensive experiments were conducted, which demonstrate that G-thinker is orders of magnitude faster than existing solution, and can scale to graphs that are two orders of magnitude larger given the same hardware resources.
Year
Venue
Field
2017
arXiv: Distributed, Parallel, and Cluster Computing
Graph,Social network,Vertex (geometry),Computer science,Matching (graph theory),Theoretical computer science,Analytics,Graph (abstract data type),Computation,Bounded function,Distributed computing
DocType
Volume
Citations 
Journal
abs/1709.03110
3
PageRank 
References 
Authors
0.42
21
6
Name
Order
Citations
PageRank
Da Yan138734.45
Hongzhi Chen24713.00
James Cheng32044101.89
M. Tamer Özsu44504582.63
Qizhen Zhang5234.74
John C. S. Lui630181.86