MarkovGNN：基于Markov扩散的图神经网络
MarkovGNN: Graph Neural Networks on Markov Diffusion
摘要：
大多数真实世界的网络都包含定义明确的社区结构，其中社区内部的节点紧密相连。为了从这些网络中学习，我们开发了MarkovGNN，它直接在不同的卷积层中捕获社区的形成和演变。与大多数图神经网络（GNN）在每一层都考虑静态图不同，MarkovGNN使用马尔可夫过程生成不同的随机矩阵，然后在不同的层中使用这些社区捕获矩阵。MarkovGNN是一种通用的方法，可以与大多数现有的GNN一起使用。我们的实验表明，MarkovGNN在聚类、节点分类和可视化任务方面优于其他GNN。MarkovGNN的源代码可在\url{https://github.com/HipGraph/MarkovGNN}上公开获得。
英文摘要：
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}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
