i2Graph: An Incremental Iterative Computation Model for Large Scale Dynamic Graphs

Due to the rapid changes in graph data sets, the mined information will quickly become obsolete, thus the entire data set needs to be re-computed from the beginning, which will result in the waste of computing time and resources. To reduce the cost of such computations, this paper proposes a model c...

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Vydáno v:2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom) s. 654 - 659
Hlavní autoři: Tang, Zhuo, He, Mengsi, Yang, Li, Fu, Zhongming
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.12.2019
Témata:
Acceleration
Big Data
Computational modeling
Data models
dynamic graph
Heuristic algorithms
incremental computation
Iterative algorithms
iterative graph algorithm
Spark GraphX
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Shrnutí:Due to the rapid changes in graph data sets, the mined information will quickly become obsolete, thus the entire data set needs to be re-computed from the beginning, which will result in the waste of computing time and resources. To reduce the cost of such computations, this paper proposes a model called i 2 Graph to support incremental iterative computation for dynamic graphs. Different from the way of traditional iteration, i 2 Graph executes the graph algorithm by reusing the results of the previous graph and performs computation on parts of the graph that has changed. i 2 Graph contains two components: (1) an incremental iterative computation model to improve the execution efficiency of the iterative graph algorithm; and (2) an incremental update method to accelerate the iterative process within the iterative graph algorithm. It is implemented based on Spark GraphX, a popular parallel and distributed computing framework for large-scale graph processing. Experiment results verify the performance advantages of i 2 Graph model when performing some iterative graph algorithms on the dynamic graph, compared with the traditional iteration.
DOI:10.1109/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00099