2018/06/04 by Beibei Si, Si, Beibei
Computer Science · #Advanced Graph Neural Networks #Data Management and Algorithms #Distributed #FOS: Computer and information sciences #Graph Theory and Algorithms #Parallel #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.1806.00907
openalex publication_date 2018/06/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
With the advent of the big data, graph are processed in an iterative manner, which incrementally described in the form of graph in big data applications. Most currently, graph processing methods treat the underlying map data as black boxes. However, as shown in experimental evaluation, graph structures often have diversity, different graph processing methods are very sensitive to the graph structure and show different performance for different data sets. Based on this, a graph processing method for graph structure analysis is proposed in this paper: (1) This paper calculates the vertex activity of a graph according to the in-degree and out-degree, and divide the corresponding vertices into the hot or cold partitions; (2) According to the change of graph structure caused by partial vertex convergence after iteration, this paper reclassifies the partitions, divides the lower active vertices into cold partition and reduces the frequency of calculation, which thereby reducing the cache miss rate and the I/O overhead caused by active vertices as well; (3) The partition with highest vertex status degree are given a priority calculation in this paper. In detail, more pronounced and more frequent vertices have higher processing priority. In this way, the convergence speed of the graph vertices is accelerated, and the running time of the graph algorithm in the big data environment is reduced. Our experiments show that compared with the latest system, the proposed method can double the performance of different graph algorithms and data sets.