论文标题
捆捆神经网络
Sheaf Neural Networks
论文作者
论文摘要
我们通过概括了该类别的图神经网络的基础扩散操作来提出图形卷积网络的概括。这些捆层神经网络基于薄片laplacian，这是图形拉普拉斯式的概括，该图编码了通过基础图参数参数的其他关系结构。 Sheaf Laplacian和相关的矩阵在图形卷积网络中提供了扩散操作的扩展版本，为节点之间的关系之间的关系是非恒定，不对称且尺寸变化的域提供了适当的概括。我们表明，所得的捆出神经网络可以在节点之间关系不对称和签名的域中胜过图形卷积网络。
We present a generalization of graph convolutional networks by generalizing the diffusion operation underlying this class of graph neural networks. These sheaf neural networks are based on the sheaf Laplacian, a generalization of the graph Laplacian that encodes additional relational structure parameterized by the underlying graph. The sheaf Laplacian and associated matrices provide an extended version of the diffusion operation in graph convolutional networks, providing a proper generalization for domains where relations between nodes are non-constant, asymmetric, and varying in dimension. We show that the resulting sheaf neural networks can outperform graph convolutional networks in domains where relations between nodes are asymmetric and signed.
