Papers published in top conferences or journals is an important measure of the innovation ability of institutions, and ranking paper acceptance rate can be helpful for evaluating affiliation potential in academic research. Most studies only focus on the paper quality itself, and apply simple statistical data to estimate the contribution of institutions. In this work, a novel method is proposed by combining different types of features of affiliation and author to predict the paper acceptance at the institutional level. Based on the history of the paper published, this work firstly calculates the affiliation scores, constructs an institutional collaboration network and analyzes the importance of the institutions using network centrality measures. Four measures about the authors’ influence and capability are then extracted to take the contributions of authors into consideration. Finally, a random forest algorithm is adopted to solve the prediction problem of paper acceptance. As a result, this paper improves the ranking of the paper acceptance rate NDCG@20 to 0.865, which is superior to other state-of-the-art approaches. The experimental results show the effectiveness of proposed method, and the information between different types of features can be complementary for predicting paper acceptance rate.

中文翻译：

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基于个体特征与网络特征相结合的机构层面论文接受度预测

在顶级会议或期刊上发表的论文是衡量机构创新能力的重要指标，论文接受率排名有助于评估学术研究的从属潜力。大多数研究只关注论文质量本身，并应用简单的统计数据来估计机构的贡献。在这项工作中，通过结合不同类型的隶属关系和作者特征，提出了一种新方法来预测机构层面的论文接受度。基于论文发表的历史，这项工作首先计算从属分数，构建机构合作网络，并使用网络中心性度量分析机构的重要性。然后提取关于作者影响力和能力的四个度量，以考虑作者的贡献。最后采用随机森林算法解决论文接受的预测问题。因此，本文将论文接受率 NDCG@20 的排名提高到 0.865，优于其他最先进的方法。实验结果表明了该方法的有效性，不同类型特征之间的信息可以互补预测论文接受率。