Graph Pattern Matching for Dynamic Team Formation

TL;DR

This paper introduces a novel graph pattern matching method for dynamic top-k team formation that considers structural constraints and capacity bounds, enabling efficient updates in evolving environments.

Contribution

It presents a new approach integrating structural constraints into team formation, along with an incremental algorithm for dynamic environments, which is a novel contribution.

Findings

Effective in real-life and synthetic datasets

Outperforms existing methods in efficiency

Handles continuous pattern and data updates

Abstract

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.