1 code implementation • 18 Nov 2019 • JianXin Li, Cheng Ji, Hao Peng, Yu He, Yangqiu Song, Xinmiao Zhang, Fanzhang Peng
However, despite the success of current random-walk-based methods, most of them are usually not expressive enough to preserve the personalized higher-order proximity and lack a straightforward objective to theoretically articulate what and how network proximity is preserved.
no code implementations • 19 Jul 2019 • Jingyuan Wang, Ning Wu, Wayne Xin Zhao, Fanzhang Peng, Xin Lin
To address these issues, we propose using neural networks to automatically learn the cost functions of a classic heuristic algorithm, namely A* algorithm, for the PRR task.