Graph Neural Networks (GNNs) are widely used for node classification, yet their opaque decision-making limits trust and adoption. While local explanations offer insights into individual predictions, global explanation methods—those that characterize an entire class—remain underdeveloped. Existing global explainers rely on motif discovery in small graphs, an approach that breaks down in large, real-world settings where subgraph repetition is rare, node attributes are high-dimensional, and predictions arise from complex structure-attribute interactions. We propose GnnXemplar, a novel global explainer inspired from Exemplar Theory from cognitive science. GnnXemplar identifies representative nodes in the GNN embedding space—exemplars—and explains predictions using natural language rules derived from their neighborhoods. Exemplar selection is framed as a coverage maximization problem over reverse $k$-nearest neighbors, for which we provide an efficient greedy approximation. To derive interpretable rules, we employ a self-refining prompt strategy using large language models (LLMs). Experiments across diverse benchmarks show that GnnXemplar significantly outperforms existing methods in fidelity, scalability, and human interpretability, as validated by a user study with 60 participants.

Operating micro aerial vehicles (MAVs) outside of the bounds of a rigidly controlled lab environment, specifically one that is unstructured and contains unknown obstacles, poses a number of challenges. One of these challenges is that of quickly determining an optimal (or nearly so) path from the MAVs current position to a designated goal state. Past work in this area using full-size unmanned aerial vehicles (UAVs) has predominantly been performed in benign environments. However, due to their small size, MAVs are capable of operating in indoor environments which are more cluttered. This requires planners to account for the vehicle heading in addition to its spatial position in order to successfully navigate. In addition, due to the short flight times of MAVs along with the inherent hazards of operating in close proximity to obstacles, we desire the trajectories to be as cost-optimal as possible. Our approach uses an anytime planner based on A* that performs a graph search on a four-dimensional (4-D) (x,y,z, heading) lattice. This allows for the generation of close-to-optimal trajectories based on a set of precomputed motion primitives along with the capability to provide trajectories in real-time allowing for on-the-fly re-planning as new sensor data is received. We also account for arbitrary vehicle shapes, permitting the use of a non-circular footprint during the planning process. By not using the overly conservative circumscribed circle for collision checking, we are capable of successfully finding optimal paths through cluttered environments including those with narrow hallways. Analytically, we show that our planner provides bounds on the sub-optimality of the solution it finds. Experimentally, we show that the planner can operate in real-time in both a simulated and real-world cluttered environments.