Architectures of Topological Deep Learning: A Survey on Topological Neural Networks
Architectures of Topological Deep Learning: A Survey on Topological Neural Networks
复制标题
DOI：
10.48550/arxiv.2304.10031
复制
发表时间：
2023-04
期刊：
影响因子：
--
通讯作者：
Mathilde Papillon;S. Sanborn;Mustafa Hajij;Nina Miolane
中科院分区：
文献类型：
--
作者：
Mathilde Papillon;S. Sanborn;Mustafa Hajij;Nina Miolane
自然界充满了复杂的系统，其特点是其组成部分之间的关系错综复杂：从社会网络中个人之间的社会相互作用到蛋白质中原子之间的静电相互作用。拓扑深度学习（TDL）提供了一个全面的框架来处理和从与这些系统相关的数据中提取知识，例如预测个体所属的社会群体或预测蛋白质是否可以成为药物开发的合理目标。TDL已经展示了理论和实践上的优势，有望在应用科学和其他领域取得突破性进展。然而，用于关系系统的TDL文献的快速增长也导致了跨消息传递拓扑神经网络（TNN）架构的符号和语言缺乏统一。这对于在现有工作的基础上构建和将消息传递tnn部署到新的现实世界问题上构成了真正的障碍。为了解决这个问题，我们对关系系统的TDL进行了简单的介绍，并比较了最近发布的使用统一数学和图形符号的消息传递tnn。通过对新兴的TDL领域的直观和批判性的回顾，我们对当前的挑战和未来发展的激动人心的机会提取了有价值的见解。
The natural world is full of complex systems characterized by intricate relations between their components: from social interactions between individuals in a social network to electrostatic interactions between atoms in a protein. Topological Deep Learning (TDL) provides a comprehensive framework to process and extract knowledge from data associated with these systems, such as predicting the social community to which an individual belongs or predicting whether a protein can be a reasonable target for drug development. TDL has demonstrated theoretical and practical advantages that hold the promise of breaking ground in the applied sciences and beyond. However, the rapid growth of the TDL literature for relational systems has also led to a lack of unification in notation and language across message-passing Topological Neural Network (TNN) architectures. This presents a real obstacle for building upon existing works and for deploying message-passing TNNs to new real-world problems. To address this issue, we provide an accessible introduction to TDL for relational systems, and compare the recently published message-passing TNNs using a unified mathematical and graphical notation. Through an intuitive and critical review of the emerging field of TDL, we extract valuable insights into current challenges and exciting opportunities for future development.