Distributed processing of large-scale graph data has many practical applications and has
been widely studied. In recent years, a lot of distributed graph processing frameworks and …

The world is becoming a more conjunct place and the number of data sources such as
social networks, online transactions, web search engines, and mobile devices is increasing …

There has been significant recent interest in parallel graph processing due to the need to
quickly analyze the large graphs available today. Many graph codes have been designed …

Existing graph-processing frameworks let users develop efficient implementations for many
graph problems, but none of them support efficiently bucketing vertices, which is needed for …

Detecting strongly connected components (SCCs) in a directed graph is crucial for
understanding the structure of graphs. Most real-world graphs have one large SCC that …

Graph processing systems are important in the big data domain. However, processing
graphs in parallel often introduces redundant computations in existing algorithms and …

Network science methodology is increasingly applied to a large variety of real-world
phenomena, often leading to big network data sets. Thus, networks (or graphs) with millions …

Computing the single-source shortest path (SSSP) is one of the fundamental graph
algorithms, and is used in many applications. Here, we focus on computing SSSP on large …

Proposed algorithms for calculating the shortest paths such as Dijikstra and Flowd-
Warshall's algorithms are limited to small networks due to computational complexity and …

Many task-based graph algorithms benefit from executing tasks according to some
programmer-specified priority order. To support such algorithms, graph frameworks use …