BScNets: Block Simplicial Complex Neural Networks
BScNets: Block Simplicial Complex Neural Networks
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DOI：
10.1609/aaai.v36i6.20583
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
Yuzhou Chen;Y. Gel;H. Poor
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文献类型：
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作者：
Yuzhou Chen;Y. Gel;H. Poor
单纯神经网络（SNN）最近已经成为图学习的一个新方向，它将卷积架构的思想从节点空间扩展到图上的单纯复合体。而不是主要评估节点之间的成对关系，在目前的实践中，单纯复形允许我们描述更高阶的相互作用和多节点图结构。通过建立在卷积运算和新的块Hodge-Laplacian之间的连接，我们提出了第一个用于链路预测的SNN。我们的新块简单复杂神经网络（BScNets）模型通过系统地整合不同维度的多个高阶图结构之间的显着交互来概括现有的图卷积网络（GCN）框架。我们讨论了BScNets背后的理论基础，并说明了它在八个真实世界和合成数据集上的链接预测实用程序。我们的实验表明，BScNets在保持低计算成本的同时，显著优于最先进的模型。最后，我们展示了BScNets作为一种新的有前途的替代方案的实用性，用于跟踪传染病（如COVID-19）的传播，并衡量医疗风险缓解策略的有效性。
Simplicial neural networks (SNNs) have recently emerged as a new direction in graph learning which expands the idea of convolutional architectures from node space to simplicial complexes on graphs. Instead of predominantly assessing pairwise relations among nodes as in the current practice, simplicial complexes allow us to describe higher-order interactions and multi-node graph structures. By building upon connection between the convolution operation and the new block Hodge-Laplacian, we propose the first SNN for link prediction. Our new Block Simplicial Complex Neural Networks (BScNets) model generalizes existing graph convolutional network (GCN) frameworks by systematically incorporating salient interactions among multiple higher-order graph structures of different dimensions. We discuss theoretical foundations behind BScNets and illustrate its utility for link prediction on eight real-world and synthetic datasets. Our experiments indicate that BScNets outperforms the state-of-the-art models by a significant margin while maintaining low computation costs. Finally, we show utility of BScNets as a new promising alternative for tracking spread of infectious diseases such as COVID-19 and measuring the effectiveness of the healthcare risk mitigation strategies.