Abstract
In this paper, we study semi-supervised graph classification, which aims at accurately predicting the categories of graphs in scenarios with limited labeled graphs and abundant unlabeled graphs. Despite the promising capability of graph neural networks (GNNs), they typically require a large number of costly labeled graphs, while a wealth of unlabeled graphs fail to be effectively utilized. Moreover, GNNs are inherently limited to encoding local neighborhood information using message-passing mechanisms, thus lacking the ability to model higher-order dependencies among nodes. To tackle these challenges, we propose a Hypergraph-Enhanced DuAL framework named HEAL for semi-supervised graph classification, which captures graph semantics from the perspective of the hypergraph and the line graph, respectively. Specifically, to better explore the higher-order relationships among nodes, we design a hypergraph structure learning to adaptively learn complex node dependencies beyond pairwise relations. Meanwhile, based on the learned hypergraph, we introduce a line graph to capture the interaction between hyperedges, thereby better mining the underlying semantic structures. Finally, we develop a relational consistency learning to facilitate knowledge transfer between the two branches and provide better mutual guidance. Extensive experiments on real-world graph datasets verify the effectiveness of the proposed method against existing state-of-the-art methods.
Abstract (translated)
在本文中，我们研究半监督图分类，旨在准确预测在有限标注图和丰富无标注图的场景中，图的类别。尽管图神经网络（GNNs）具有鼓舞人心的能力，但它们通常需要大量昂贵的标注图，而大量的无标注图却无法有效利用。此外，GNNs本身有限于使用消息传递机制编码局部邻近信息，因此缺乏对节点之间高级依赖的建模能力。为了应对这些挑战，我们提出了一个名为HEAL的半监督图分类框架，从图和线图的角度分别捕捉图的语义。具体来说，为了更好地探索节点之间的更高阶关系，我们设计了一种自适应图结构学习方法，以学习超越一对一关系的复杂节点依赖。同时，基于学到的超图，我们引入了线图，以捕捉超边之间的关系，从而更好地挖掘潜在的语义结构。最后，我们开发了一种关系一致性学习方法，以促进两个分支之间的知识传递，并提供更好的相互指导。在现实世界的图数据集上进行的大量实验证实了所提出方法的有效性，与现有最先进的方法相比具有竞争力的优势。
URL
https://arxiv.org/abs/2405.04773
