论文标题
通过增强决策树和时光图神经网络来预测人工智能的研究趋势
Predicting Research Trends in Artificial Intelligence with Gradient Boosting Decision Trees and Time-aware Graph Neural Networks
论文作者
论文摘要
Science4cast 2021竞赛的重点是预测不断发展的语义网络中的未来边缘，每个顶点代表一个人工智能概念，一对顶点之间的优势表示这两个概念已经在科学论文中一起研究了。在本文中，我们描述了我们对这项比赛的解决方案。我们提出了两种不同的方法：一种基于树的梯度提升方法和一种深度学习方法，并证明这两种方法都达到了竞争性能。我们的最终解决方案基于两种方法的融合，在所有参与团队中获得了第一名。本文的源代码可从https://github.com/yichaolu/science4cast2021获得。
The Science4cast 2021 competition focuses on predicting future edges in an evolving semantic network, where each vertex represents an artificial intelligence concept, and an edge between a pair of vertices denotes that the two concepts have been investigated together in a scientific paper. In this paper, we describe our solution to this competition. We present two distinct approaches: a tree-based gradient boosting approach and a deep learning approach, and demonstrate that both approaches achieve competitive performance. Our final solution, which is based on a blend of the two approaches, achieved the 1st place among all the participating teams. The source code for this paper is available at https://github.com/YichaoLu/Science4cast2021.
