Abstract : Several distributed frameworks have recently emerged to perform computations on large-scale graphs. However some
recent studies have highlighted that vertex-partitioning approaches, e.g. Giraph, failed to achieve workload-balanced
partitioning for skewed graphs, typically having a heavy-
tail degree distribution. While edge-partitioning approaches
such as PowerGraph and GraphX provide better balancing and performances for graph computation, they supply a
generic framework, independent from the computation. This
demonstration presents SGVCut to display our edge partitions designed for random walks-based computation, which
is the foundation of many graph algorithms, on skewed graphs.
The demonstration scenario introduces SGVCut interface and
illustrates the benefits of our approach compare to other partitioning strategies for different settings and algorithms.
