i2Graph: An Incremental Iterative Computation Model for Large Scale Dynamic Graphs - Tang, Zhuo; He, Mengsi; Yang, Li; Fu, Zhongming (2019 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), 2019)
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.