  Finding a list of k teams of experts, referred to as top-k team formation,
with the required skills and high collaboration compatibility has been
extensively studied. However, existing methods have not considered the specific
collaboration relationships among different team members, i.e., structural
constraints, which are typically needed in practice. In this study, we first
propose a novel graph pattern matching approach for top-k team formation, which
incorporates both structural constraints and capacity bounds. Second, we
formulate and study the dynamic top-k team formation problem due to the growing
need of a dynamic environment. Third, we develop an unified incremental
approach, together with an optimization technique, to handle continuous pattern
and data updates, separately and simultaneously, which has not been explored
before. Finally, using real-life and synthetic data, we conduct an extensive
experimental study to show the effectiveness and efficiency of our graph
pattern matching approach for (dynamic) top-k team formation.
