Graph Analysis Using a GPU-based Parallel Algorithm: Quantum Clustering

Graph Clustering, also known as network clustering, is a technique for partitioning a graph into clusters or communities of nodes based on their structural properties[1]. Graph clustering is used in various applications such as social network analysis, image segmentation, bioinformatics, and more. The goal of graph clustering is to group the nodes in a way to maximizes the similarity within the group and minimizes the similarity between them[2]. These two similarities are usually measured using various metrics such as modularity, Normalized Mutual Information(NMI), Adjusted Rand Index(ARI) and FowlkesMallows Index(FMI).