With the exponential growth of graph structured data in recent years, parallel distributed
techniques play an increasingly important role in processing large-scale graphs. Since …

We propose Distributed Neighbor Expansion (Distributed NE), a parallel and distributed
graph partitioning method that can scale to trillion-edge graphs while providing high …

Graph partitioning is a mandatory step in large-scale distributed graph processing. When
partitioning real-world power-law graphs, the edge partitioning algorithm performs better …

Graph edge partitioning, which is essential for the efficiency of distributed graph computation
systems, divides a graph into several balanced partitions within a given size to minimize the …

The increasing importance of graph data in various fields requires large-scale graph data to
be processed efficiently. Furthermore, well-balanced graph partitioning is a vital component …

Recently, graph data become increasingly important and larger in many fields, and many
distributed graph computing systems have been proposed to deal with large-scale graphs …

Graph processing has become an integral part of big data analytics. With the ever increasing
size of the graphs, one needs to partition them into smaller clusters, which can be managed …

Graph Partitioning is widely used in many real-world applications such as fraud detection
and social network analysis, in order to enable the distributed graph computing on large …

Graph computing plays an important role in mining data at large scale. Partition is the
primary step when we process large graph in a distributed system. A good partition has less …

One of the concepts that have attracted attention since entering the big data era is graph-
structured data. Distributed systems for graph analysis are widely used to process large …