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 ...

This paper focuses on a graph theory-based spectral clustering approach for critical infrastructure management on DER integrated power distribution system. The main goal is to develop a virtual ...

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 ...

Extracting knowledge by performing computations on graphs is becoming increasingly challenging as graphs grow in size. A standard approach distributes the graph over a cluster of nodes, but performing ...

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 ...