Challenges in large-graph processing: A vision

As a representation of high connected objects, graphs receive a arising attention. By virtue of the interconnection of graph data, current general-purpose parallel data processing systems misfit effectively graph processing. Thus, a wide spectrum of dedicated graph processing system emerged. In this paper, we give a guidance of classical types of graph processing system. We discuss key features and the according challenges of graph processing from the aspect of graph data, graph algorithm as well as the computation implementation. Then we specify four strategies that should be taken into account when designing a graph processing systems. In the last part of our paper we make a comparison of present typical graph processing systems and specify their suitable application area.

[1]  Alexandru Iosup,et al.  Benchmarking graph-processing platforms: a vision , 2014, ICPE.

[2]  Wilfred Ng,et al.  Blogel: A Block-Centric Framework for Distributed Computation on Real-World Graphs , 2014, Proc. VLDB Endow..

[3]  Willy Zwaenepoel,et al.  Scale-up graph processing in the cloud: challenges and solutions , 2014, CloudDP '14.

[4]  Guy E. Blelloch,et al.  GraphChi: Large-Scale Graph Computation on Just a PC , 2012, OSDI.

[5]  Michael W. Mahoney,et al.  Algorithmic and statistical challenges in modern largescale data analysis are the focus of MMDS 2008 , 2008, SKDD.

[6]  Zhihua Zhang,et al.  Distributed Power-law Graph Computing: Theoretical and Empirical Analysis , 2014, NIPS.

[7]  Jonathan W. Berry,et al.  Challenges in Parallel Graph Processing , 2007, Parallel Process. Lett..

[8]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[9]  Christos Faloutsos,et al.  Graph mining: Laws, generators, and algorithms , 2006, CSUR.

[10]  Haixun Wang,et al.  Trinity: a distributed graph engine on a memory cloud , 2013, SIGMOD '13.

[11]  Willy Zwaenepoel,et al.  X-Stream: edge-centric graph processing using streaming partitions , 2013, SOSP.

[12]  Aart J. C. Bik,et al.  Pregel: a system for large-scale graph processing , 2010, SIGMOD Conference.

[13]  Joseph Gonzalez,et al.  PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs , 2012, OSDI.

[14]  Shirish Tatikonda,et al.  From "Think Like a Vertex" to "Think Like a Graph" , 2013, Proc. VLDB Endow..

[15]  Duen Horng Chau,et al.  Leveraging memory mapping for fast and scalable graph computation on a PC , 2013, 2013 IEEE International Conference on Big Data.

[16]  Mohammed J. Zaki,et al.  Arabesque: a system for distributed graph mining , 2015, SOSP.