Session

DescriptionEstimating the structure, partitioning, and analyzing graphs, are all critical tasks in a plethora of applications. Problems in domains such as image processing, social network analysis, and classification via neural networks, are often formulated as being graph-based. Simultaneously, graph analytic methods are traditionally an important subtask that enables the parallelization or the complexity reduction of the entire algorithmic workflow. This minisymposium samples recent advances in methods intended for graphs emerging from large-scale data, with a focus on performant, efficient, and scalable algorithms.