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Scientific Programming
Volume 2017 (2017), Article ID 2573592, 11 pages
https://doi.org/10.1155/2017/2573592
Research Article

An Association-Oriented Partitioning Approach for Streaming Graph Query

Yun Hao,1,2,3 Gaofeng Li,1,2,3 Pingpeng Yuan,1,2,3 Hai Jin,1,2,3 and Xiaofeng Ding1,2,3

1Services Computing Technology and System Lab., School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
2Cluster and Grid Computing Lab., School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
3Big Data Technology and System Lab., School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China

Correspondence should be addressed to Pingpeng Yuan; nc.ude.tsuh.liam@nauypp and Hai Jin; nc.ude.tsuh@nijh

Received 26 December 2016; Accepted 11 April 2017; Published 25 May 2017

Academic Editor: Alex M. Kuo

Copyright © 2017 Yun Hao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The volumes of real-world graphs like knowledge graph are increasing rapidly, which makes streaming graph processing a hot research area. Processing graphs in streaming setting poses significant challenges from different perspectives, among which graph partitioning method plays a key role. Regarding graph query, a well-designed partitioning method is essential for achieving better performance. Existing offline graph partitioning methods often require full knowledge of the graph, which is not possible during streaming graph processing. In order to handle this problem, we propose an association-oriented streaming graph partitioning method named Assc. This approach first computes the rank values of vertices with a hybrid approximate PageRank algorithm. After splitting these vertices with an adapted variant affinity propagation algorithm, the process order on vertices in the sliding window can be determined. Finally, according to the level of these vertices and their association, the partition where the vertices should be distributed is decided. We compare its performance with a set of streaming graph partition methods and METIS, a widely adopted offline approach. The results show that our solution can partition graphs with hundreds of millions of vertices in streaming setting on a large collection of graph datasets and our approach outperforms other graph partitioning methods.