The first step to parallelize graph algorithms in distributed graph databases is to decide how to partition the graph data across the cluster. There are two main types of partitioning strategies ...

Distributed graph analysis usually partitions a large graph into multiple small-sized subgraphs and distributes them into a cluster of machines for computing. Therefore, graph partitioning plays a ...

The rapid growth of large-scale datasets in fields like biology and social networks has driven the need for advanced graph analytics techniques. Community detection, a fundamental task in graph ...

Distributed graph analysis usually partitions a large graph into multiple small-sized subgraphs and distributes them into a cluster of machines for computing. Therefore, graph partitioning plays a ...