We introduce a new algorithm for modularity-based community detection in large networks.
The algorithm, which we refer to as a smart local moving algorithm, takes advantage of a …

Abstract Quantum Bridge Analytics relates generally to methods and systems for hybrid
classical-quantum computing, and more particularly is devoted to develo** tools for …

Abstract The Clique Partitioning Problem (CPP) seeks to decompose a set of vertices into
disjoint subsets (cliques) maximizing the sum of edge weights over all the cliques. The …

Community detection is one way to reduce the complexity of analyzing networks, especially
with their rapid growth. Dividing networks into communities can help analysts and experts to …

The modularity index (Q) is an important criterion for many community detection heuristics
used in network psychometrics and its subareas (eg, exploratory graph analysis). Some …

Community detection, which involves partitioning nodes within a network, has widespread
applications across computational sciences. Modularity-based algorithms identify …

We propose a modularity optimization method, Mod-CSA, based on stochastic global
optimization algorithm, conformational space annealing (CSA). Our method outperforms …

Data are often represented as graphs. Many common tasks in data science are based on
distances between entities. While some data science methodologies natively take graphs as …