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Graph disentangling is a new promising direction that can help us to discover the latent patterns in the data and understand the behaviors of a graph learning model. Despite the many efforts in disentangling representation learning, few works focus on disentangling the latent factors behind a graph. Most current foci are mainly on studying node-level semantics in the graphs. Compared with node-level, the structure-level view can provide a new interpretable and in-depth insight into graph data. The study of structure-level relations enables us to reveal the high-order structural semantics in the data. To explore the complex high-order structural semantics in the data, we propose the High-order Structural Semantic Disentangled Neural Network (HSDN) to model the graph structure units and disentangle structural semantics. It's the first attempt to hypergraph disentangled networks. Unlike prior methods that disentangle factor graphs based on pair-wise relations only, we introduce hyperedges on pair-wise graphs to model structure units and disentangle the complex high-order structural semantics between different structures. Extensive experiments demonstrate that HSDN achieves state-of-the-art performances in terms of both disentangling and downstream tasks.

Graph-level representation learning is the pivotal step for downstream tasks that operate on the whole graph. The most common approach to this problem is graph pooling, where node features are typically averaged or summed to obtain the graph representations. However, pooling operations like averaging or summing inevitably cause severe information missing, which may severely downgrade the final performance. In this article, we argue what is crucial to graph-level downstream tasks includes not only the topological structure but also the distribution from which nodes are sampled. Therefore, powered by existing Graph Neural Networks (GNN), we propose a new plug-and-play pooling module, termed as Distribution Knowledge Embedding (DKEPool), where graphs are viewed as distributions on top of GNNs and the pooling goal is to summarize the entire distribution information instead of retaining a certain feature vector by simple predefined pooling operations. A DKEPool network de facto disassembles representation learning into two stages, structure learning and distribution learning. Structure learning follows a recursive neighborhood aggregation scheme to update node features where structure information is obtained. Distribution learning, on the other hand, omits node interconnections and focuses more on the distribution depicted by all the nodes. Extensive experiments on graph classification tasks demonstrate that the proposed DKEPool significantly and consistently outperforms the state-of-the-art methods. The code is avaliable at https://github.com/chenchkx/dkepool