Neighborhood Selection

Neighborhood selection, a crucial aspect of various machine learning tasks, focuses on strategically choosing relevant data points or nodes within a graph to improve model performance and efficiency. Current research emphasizes developing sophisticated algorithms, including those based on large neighborhood search, graph neural networks, and reinforcement learning, to optimize neighborhood selection for diverse applications such as graph anomaly detection, multi-agent pathfinding, and knowledge graph completion. These advancements aim to address challenges like computational complexity, handling skewed data distributions, and improving the robustness and explainability of models, ultimately leading to more accurate and efficient solutions in various domains.