Dynamic Hypergraph Neural Networks
Dynamic Hypergraph Neural Networks
复制标题
DOI：
10.24963/ijcai.2019/366
复制
发表时间：
2019-08
期刊：
影响因子：
--
通讯作者：
Jianwen Jiang;Yuxuan Wei;Yifan Feng;Jingxuan Cao;Yue Gao
中科院分区：
文献类型：
--
作者：
Jianwen Jiang;Yuxuan Wei;Yifan Feng;Jingxuan Cao;Yue Gao
近年来，基于图/超图的深度学习方法受到了研究人员的广泛关注。这些深度学习方法将图/超图结构作为模型中的先验知识。然而，隐藏的和重要的关系并没有直接表现在内在结构中。为了解决这个问题，我们提出了一个动态超图神经网络框架（DHGNN），该框架由两个模块的堆叠层组成：动态超图构建（DHG）和超图卷积（HGC）。考虑到最初构造的超图可能不是数据的合适表示，DHG模块在每层上动态更新超图结构。然后引入超图卷积对超图结构中的高阶数据关系进行编码。HGC模块包括顶点卷积和超边缘卷积两个阶段，分别用于顶点和超边缘之间的特征聚合。我们已经在标准数据集、Cora引文网络和微博数据集上评估了我们的方法。我们的方法优于最先进的方法。通过实验验证了该方法对不同数据分布的有效性和鲁棒性。
In recent years, graph/hypergraph-based deep learning methods have attracted much attention from researchers. These deep learning methods take graph/hypergraph structure as prior knowledge in the model. However, hidden and important relations are not directly represented in the inherent structure. To tackle this issue, we propose a dynamic hypergraph neural networks framework (DHGNN), which is composed of the stacked layers of two modules: dynamic hypergraph construction (DHG) and hypergrpah convolution (HGC). Considering initially constructed hypergraph is probably not a suitable representation for data, the DHG module dynamically updates hypergraph structure on each layer. Then hypergraph convolution is introduced to encode high-order data relations in a hypergraph structure. The HGC module includes two phases: vertex convolution and hyperedge convolution, which are designed to aggregate feature among vertices and hyperedges, respectively. We have evaluated our method on standard datasets, the Cora citation network and Microblog dataset. Our method outperforms state-of-the-art methods. More experiments are conducted to demonstrate the effectiveness and robustness of our method to diverse data distributions.