Redefining Event Types and Group Evolution in Temporal Data

arXiv:2403.06771v1 Announce Type: new
Abstract: Groups -- such as clusters of points or communities of nodes -- are fundamental when addressing various data mining tasks. In temporal data, the predominant approach for characterizing group evolution has been through the identification of ``events". However, the events usually described in the literature, e.g., shrinks/growths, splits/merges, are often arbitrarily defined, creating a gap between such theoretical/predefined types and real-data group observations. Moving beyond existing taxonomies, we think of events as ``archetypes" characterized by …

abstract arxiv communities cs.lg cs.si data data mining event events evolution however identification literature mining nodes tasks temporal through type types