Next Article in Journal
From Theory to Practice: A Data Quality Framework for Classification Tasks
Next Article in Special Issue
A Quick Gbest Guided Artificial Bee Colony Algorithm for Stock Market Prices Prediction
Previous Article in Journal
Analysis of the Angular Dependence of Time Delay in Gravitational Lensing
Previous Article in Special Issue
Research on Electronic Voltage Transformer for Big Data Background
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle
Symmetry 2018, 10(7), 247;

A Local Approximation Approach for Processing Time-Evolving Graphs

Department of Computer Science and Technology, Xi’an Jiaotong University, No 28, Xianning West Road, Xi’an 710049, China
These authors contributed equally to this work.
Author to whom correspondence should be addressed.
Received: 21 April 2018 / Revised: 8 June 2018 / Accepted: 12 June 2018 / Published: 1 July 2018
(This article belongs to the Special Issue Emerging Approaches and Advances in Big Data)
Full-Text   |   PDF [609 KB, uploaded 1 July 2018]   |  


To efficiently process time-evolving graphs where new vertices and edges are inserted over time, an incremental computing model, which processes the newly-constructed graph based on the results of the computation on the outdated graph, is widely adopted in distributed time-evolving graph computing systems. In this paper, we first experimentally study how the results of the graph computation on the local graph structure can approximate the results of the graph computation on the complete graph structure in distributed environments. Then, we develop an optimization approach to reduce the response time in bulk synchronous parallel (BSP)-based incremental computing systems by processing time-evolving graphs on the local graph structure instead of on the complete graph structure. We have evaluated our optimization approach using the graph algorithms single-source shortest path (SSSP) and PageRankon the Amazon Elastic Compute Cloud(EC2), a central part of’s cloud-computing platform, with different scales of graph datasets. The experimental results demonstrate that the local approximation approach can reduce the response time for the SSSP algorithm by 22% and reduce the response time for the PageRank algorithm by 7% on average compared to the existing incremental computing framework of GraphTau. View Full-Text
Keywords: distributed computing; time-evolving graph computing; incremental computing distributed computing; time-evolving graph computing; incremental computing

Figure 1

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 (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Ji, S.; Zhao, Y. A Local Approximation Approach for Processing Time-Evolving Graphs. Symmetry 2018, 10, 247.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Symmetry EISSN 2073-8994 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top