论文标题
一种预测人工智能论文语义关系的方法
A Method to Predict Semantic Relations on Artificial Intelligence Papers
论文作者
论文摘要
通过许多实际应用，预测大型不断发展的网络中链接的出现是一项艰巨的任务。最近，Science4Cast竞赛说明了这一挑战，该挑战提出了一个64.000 AI概念的网络，并要求参与者预测将来将一起研究哪些主题。在本文中，我们根据新的深度学习方法（即图形神经网络）提出了解决这个问题的解决方案。挑战的结果表明，即使我们不得不施加严格的限制才能获得计算高效且简约的模型，我们的解决方案也具有竞争力：忽略图形的固有动力学，而仅使用目标链接周围的一个节点的一小部分。本文提出的初步实验表明，该模型正在学习两个相关但不同的模式：通过子图和更致密的子图的汇聚和结合的节点的吸收。该模型似乎在识别第一种模式方面表现出色。
Predicting the emergence of links in large evolving networks is a difficult task with many practical applications. Recently, the Science4cast competition has illustrated this challenge presenting a network of 64.000 AI concepts and asking the participants to predict which topics are going to be researched together in the future. In this paper, we present a solution to this problem based on a new family of deep learning approaches, namely Graph Neural Networks. The results of the challenge show that our solution is competitive even if we had to impose severe restrictions to obtain a computationally efficient and parsimonious model: ignoring the intrinsic dynamics of the graph and using only a small subset of the nodes surrounding a target link. Preliminary experiments presented in this paper suggest the model is learning two related, but different patterns: the absorption of a node by a sub-graph and union of more dense sub-graphs. The model seems to excel at recognizing the first type of pattern.
