Session

DescriptionCommunity detection aims to identify clusters of closely inter-connected nodes within a network – a problem that is encountered in numerous diverse domains, from marketing and forecasting to virtually every scientific field. Many of these applications utilize networks to model their data by representing objects of interest as nodes and pairwise relationships between these objects as edges between the nodes. A pressing issue for network clustering is the inability to scale to accommodate massive datasets while ensuring reliable results. Another, subtler but highly impactful, issue is the inconsistency of a fundamental definition of 'community' in divergent domains. Expectations of sphericity or Euclidean space and neglect of singleton nodes that do not belong in any cluster are common pitfalls compromising the accuracy of results. This minisymposium will explore recent advances for a variety of algorithms that exploit high-performance computing while scrutinizing underlying assumptions. A key theme will be to dissipate the 'one size fits all' tendency by contrasting algorithms, their objectives, and their computational limits.