Disentangled Graph Convolutional Network (DisenGCN) is an encouraging framework to disentangle the latent factors arising in a real-world graph. However, it relies on disentangling information heavily from a local range (i.e., a node and its 1-hop neighbors), while the local information in many cases can be uneven and incomplete, hindering the interpretabiliy power and model performance of DisenGCN. In this paper\footnote{This paper is a lighter version of \href{https://jingweio.github.io/assets/pdf/tnnls22.pdf}{"Learning Disentangled Graph Convolutional Networks Locally and Globally"} where the results and analysis have been reworked substantially. Digital Object Identifier \url{https://doi.org/10.1109/TNNLS.2022.3195336}.}, we introduce a novel Local and Global Disentangled Graph Convolutional Network (LGD-GCN) to capture both local and global information for graph disentanglement. LGD-GCN performs a statistical mixture modeling to derive a factor-aware latent continuous space, and then constructs different structures w.r.t. different factors from the revealed space. In this way, the global factor-specific information can be efficiently and selectively encoded via a message passing along these built structures, strengthening the intra-factor consistency. We also propose a novel diversity promoting regularizer employed with the latent space modeling, to encourage inter-factor diversity. Evaluations of the proposed LGD-GCN on the synthetic and real-world datasets show a better interpretability and improved performance in node classification over the existing competitive models. Code is available at \url{https://github.com/jingweio/LGD-GCN}.

翻译：解耦图卷积网络（DisenGCN）是一种能够有效解耦真实世界图中潜在因素的有前景框架。然而，该方法严重依赖局部范围（即节点及其一阶邻居）的信息解耦，而许多情况下局部信息可能不均匀或不完整，这限制了DisenGCN的可解释性和模型性能。本文提出一种新颖的局部与全局解耦图卷积网络（Local and Global Disentangled Graph Convolutional Network, LGD-GCN），旨在捕获局部与全局信息以实现图解耦。LGD-GCN通过统计混合建模推导出因子感知的潜在连续空间，并基于该空间为不同因子构建不同的结构。通过沿这些构建的结构传递消息，可高效且选择性地编码全局因子特定信息，从而增强因子内一致性。此外，我们提出一种与潜在空间建模相结合的新型多样性促进正则化器，以鼓励因子间多样性。在合成数据集和真实数据集上的评估表明，所提出的LGD-GCN在节点分类任务中比现有竞争模型具有更好的可解释性和性能。代码见https://github.com/jingweio/LGD-GCN.