net4Lap: Neural Laplacian Regularization for Ranking and Re-Ranking

In this paper, we propose net4Lap, a novel architecture for Laplacian-based ranking. The two main ingredients of the approach are: a) pre-processing graphs with neural embed-dings before performing Laplacian ranking, and b) introducing a global measure of centrality to modulate the diffusion process. We explicitly formulate ranking as an optimization problem where regularization is emphasized. This formulation is a theoretical tool to validate our approach. Finally, our experiments show that the proposed architecture significantly outperforms state-of-the-art rankers and it is also a proper tool for re-ranking.