Authors: <strong>Wenfei Fan</strong>, Muyang Liu, Ping Lu, and Qiang Yin
Name of Conference: IEEE Transactions on Knowledge and Data Engineering（TKDE 2021）
Date of Publication: July, 2021
Graph computations often have to be conducted in parallel on partitioned graphs. The choice of graph partitioning strategies, however, has strong impact on the design of graph computation algorithms. A graph algorithm developed under edge-cut partitions may not work correctly under vertex-cut, and vice versa. We often have to rewrite our algorithms when we switch from, e.g., edge-cut to vertex-cut. To cope with this, we propose a notion of partition transparency, such that graph algorithms are able to work correctly under different partitions without changes and moreover, benefit from recent hybrid partitions to speed up computations. Furthermore, we identify conditions under which graph algorithms are guaranteed to be partition-transparent, in graph-centric and vertex-centric models. We show that a variety of graph algorithms can be made partition-transparent. Using real-life and synthetic graphs, we experimentally verify that partition-transparent algorithms compute correct answers under different partitions; better still, under hybrid partitions these algorithms perform better than algorithms tailored for edge-cut and vertex-cut partitions in efficiency.