论文标题
Markovgnn：马尔可夫扩散上的图形神经网络
MarkovGNN: Graph Neural Networks on Markov Diffusion
论文作者
论文摘要
大多数现实世界网络都包含定义明确的社区结构，在社区内部内部连接节点。要从这些网络中学习，我们开发了Markovgnn，该网络直接在不同的卷积层中捕获了社区的形成和演变。与大多数考虑每一层静态图的图形神经网络（GNN）不同，Markovgnn使用Markov进程生成不同的随机矩阵，然后在不同层中使用这些社区捕获矩阵。 Markovgnn是一种可以与大多数现有GNN一起使用的通用方法。我们通过实验表明，Markovgnn优于其他GNN用于聚类，节点分类和可视化任务。 Markovgnn的源代码可在\ url {https://github.com/hipgraph/markovgnn}上公开获得。
Most real-world networks contain well-defined community structures where nodes are densely connected internally within communities. To learn from these networks, we develop MarkovGNN that captures the formation and evolution of communities directly in different convolutional layers. Unlike most Graph Neural Networks (GNNs) that consider a static graph at every layer, MarkovGNN generates different stochastic matrices using a Markov process and then uses these community-capturing matrices in different layers. MarkovGNN is a general approach that could be used with most existing GNNs. We experimentally show that MarkovGNN outperforms other GNNs for clustering, node classification, and visualization tasks. The source code of MarkovGNN is publicly available at \url{https://github.com/HipGraph/MarkovGNN}.
