论文标题
简单2复杂卷积神经网
Simplicial 2-Complex Convolutional Neural Nets
论文作者
论文摘要
最近，已经开发了神经网络架构，以适应数据的结构或更一般的超图。虽然有用，但图形结构可能会受到限制。一般而言，HyperGraph结构不会考虑其超蛋白质之间的高阶关系。简单的复合物提供了中间立场，具有丰富的理论。我们在简单的2个复合物上开发了卷积神经网络层。
Recently, neural network architectures have been developed to accommodate when the data has the structure of a graph or, more generally, a hypergraph. While useful, graph structures can be potentially limiting. Hypergraph structures in general do not account for higher order relations between their hyperedges. Simplicial complexes offer a middle ground, with a rich theory to draw on. We develop a convolutional neural network layer on simplicial 2-complexes.
