지식 그래프에서 깊이 우선 탐색(DFS)과 너비 우선 탐색(BFS)을 결합한 새로운 접근법을 제안하여 링크 예측 성능을 향상시킬 수 있다.
A novel approach that integrates centrality measures with classical machine learning methods to enhance link prediction in knowledge graphs by leveraging the graph's topology through depth-first and breadth-first search techniques.
A lightweight Graph Inception Diffusion Network (GIDN) model that generalizes graph diffusion in different feature spaces and uses the inception module to avoid the large computational cost of complex network structures, achieving high-efficiency link prediction.