Performance evaluation and prediction of academic achievements is an essential task for scientists, research organizations, research funding bodies, and government agencies alike. Recently, heterogeneous networks have been used to evaluate or predict performance of multi-entities including papers, researchers, and venues with some success. However, only a minimum of effort has been made to predict the future influence of papers, researchers and venues. In this paper, we propose a new framework WMR-Rank for this purpose. Based on the dynamic and heterogeneous network of multiple entities, we extract seven types of relations among them. The framework supports useful features including the refined granularity of relevant entities such as authors and venues, time awareness for published papers and their citations, differentiating the contribution of multiple coauthors to the same paper, amongst others. By leveraging all seven types of relations and fusing the rich information in a mutually reinforcing style, we are able to predict future influence of papers, authors and venues more precisely. Using the ACL dataset, our experimental results demonstrate that the proposed approach considerably outperforms state-of-the art competitors.

绩效评估和学术成就的预测对于科学家，研究组织，研究资助机构和政府机构都是至关重要的任务。最近，异构网络已用于评估或预测包括论文，研究人员和场所在内的多实体的性能，并取得了一些成功。但是，仅作了最小的努力就可以预测论文，研究人员和场所的未来影响。在本文中，我们为此提出了一个新的框架WMR-Rank。基于多个实体的动态异构网络，我们提取了它们之间的七种关系。该框架支持有用的功能，包括相关实体（例如作者和地点）的精细粒度，对已发表论文的时间认识及其引用，区分多个共同作者对同一篇论文的贡献等。通过利用所有七种类型的关系并以相辅相成的方式融合丰富的信息，我们能够更准确地预测论文，作者和会场的未来影响。使用ACL数据集，我们的实验结果表明，提出的方法大大优于最新的竞争对手。