Foundations of Graph Representation Learning in the context of Network and Life Sciences

网络和生命科学背景下的图表示学习基础

• 批准号: 2719165
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2719165
• 关键词: Foundations; Graph; Representation; Learning; context

Graph Neural Networks are specific networks for learning on data with the graph structure. Applications of them arenumerous in fields like biology or social networks. Though extremely useful, they suffer from the assumption that graphsdo not change in time. The new frameworks considering the dynamic nature of graphs are being developed, but theyalso do not offer a full picture of graph's dynamics. They often consider a narrow range of possible events in the graph'sevolution, such as edge insertion, but often ignore events like node insertions and deletions or changing edge features.Extension to all types of events would be my research focus. The complete deep learning model that faithfully representsthe real-world data could be applied to predicting the future states of the graph. It also opens a possibility to improvesolving semi-supervised or unsupervised problems. In this research, we would like to investigate how the knowledge aboutthe future state of the graph can be leveraged to improve performance in tasks concerning its current state. Moreover, itis worth noting that the ideas associated with representation learning on dynamic graphs can be applied outside of thisdomain. Hence insights from improving the temporal Graph Neural Networks could be potentially applied in other areas,such as dynamic graph generation.

图神经网络是用于使用图形结构的数据学习的特定网络。它们在生物学或社交网络等领域的应用。尽管非常有用，但它们却遭受了Graphsdo不会随时间变化的假设。考虑到图形的动态性质的新框架正在开发中，但它们也没有提供图形动力学的完整图片。他们经常考虑图形卷积中的可能发生的狭窄范围，例如边缘插入，但通常会忽略诸如节点插入和删除或更改边缘功能之类的事件。对所有类型的事件的扩展将是我的研究重点。忠实地代表现实世界数据的完整深度学习模型可以应用于预测图的未来状态。这也为改善半监督或无监督的问题提供了可能性。在这项研究中，我们想研究如何利用有关图表未来状态的知识来提高有关其当前状态的任务的绩效。此外，值得注意的是，可以在此域之外应用与表示动态图的表示的想法。因此，可以在其他领域（例如动态图生成）中使用改善时间图神经网络来洞悉。