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
摘要：
预测大型演化网络中链路的出现是一项具有许多实际应用的艰巨任务。最近，Science4cast竞赛展示了这一挑战，展示了一个由64，000个AI概念组成的网络，并要求参与者预测未来将共同研究哪些主题。在本文中，我们提出了一种基于一种新的深度学习方法的解决方案，即图神经网络。挑战的结果表明，我们的解决方案是有竞争力的，即使我们不得不施加严格的限制，以获得一个计算效率和简约的模型：忽略图的内在动力学，只使用一个小的节点子集周围的目标链接。本文提出的初步实验表明，该模型正在学习两种相关但不同的模式：子图对节点的吸收和更密集子图的联合。该模型似乎擅长识别第一种类型的模式。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
