Neighborhood Reconstruction Methods

Reconstructing neighborhoods is a critical step in recovering and refining the hidden structure of graph data, especially when networks are incomplete, noisy, or partially observed. This chapter presents neighborhood reconstruction as both a learning problem and a structural inference task, aimed at restoring meaningful connections between nodes. It is introduced by reconstruction techniques, including matrix factorization, embedding-based modes, and similarity-driven methods, which estimate missing or uncertain links from observed patterns. A detailed encoder-decoder framework is then explored to show how latent graph representations can be learned and used to rebuild local neighborhood structures. By integrating the concept with real-world scenarios, the chapter highlights how neighborhood reconstruction enhances graph reliability, predictive accuracy, and decision-making in complex networked systems.