基于层次聚合和关系度量学习的树结构感知图表示学习
Tree Structure-Aware Graph Representation Learning via Integrated Hierarchical Aggregation and Relational Metric Learning
摘要：
虽然图神经网络（GNN）在学习同构图的节点表示方面表现出了优越性，但在异构图上利用GNN仍然是一个具有挑战性的问题。主要原因是GNN通过聚合邻居的信息来学习节点表示，而不管节点类型如何。一些工作提出了缓解这样的问题，利用关系或元路径的样本具有不同的类别的邻居，然后使用注意力机制学习不同的重要性为不同的类别。然而，一个限制是，不同类型的节点的学习表示应该拥有不同的特征空间，而所有上述工作仍然将节点表示投影到一个特征空间中。此外，在探索了大量的异构图之后，我们发现了一个事实，即具有相同类型的多个节点总是连接到具有另一种类型的节点，这揭示了多对一模式，即。层次树结构。但是，所有上述工作都不能保持这种树结构，因为从邻居到目标节点的精确多跳路径相关性将通过聚合而被擦除。因此，为了克服文献的局限性，我们提出了T-GNN，一种用于图表示学习的树结构感知图神经网络模型。具体来说，提出的T-GNN由两个模块组成：（1）集成的层次聚合模块和（2）关系度量学习模块。集成的分层聚合模块旨在通过将GNN与门控递归单元相结合以将树结构上的分层和顺序邻域信息集成到节点表示来保持树结构。关系度量学习模块的目的是保持异构性嵌入到一个特定类型的空间，具有不同的分布基于相似性度量的每种类型的节点。
英文摘要：
While Graph Neural Network (GNN) has shown superiority in learning node representations of homogeneous graphs, leveraging GNN on heterogeneous graphs remains a challenging problem. The dominating reason is that GNN learns node representations by aggregating neighbors' information regardless of node types. Some work is proposed to alleviate such issue by exploiting relations or meta-path to sample neighbors with distinct categories, then use attention mechanism to learn different importance for different categories. However, one limitation is that the learned representations for different types of nodes should own different feature spaces, while all the above work still project node representations into one feature space. Moreover, after exploring massive heterogeneous graphs, we identify a fact that multiple nodes with the same type always connect to a node with another type, which reveals the many-to-one schema, a.k.a. the hierarchical tree structure. But all the above work cannot preserve such tree structure, since the exact multi-hop path correlation from neighbors to the target node would be erased through aggregation. Therefore, to overcome the limitations of the literature, we propose T-GNN, a tree structure-aware graph neural network model for graph representation learning. Specifically, the proposed T-GNN consists of two modules: (1) the integrated hierarchical aggregation module and (2) the relational metric learning module. The integrated hierarchical aggregation module aims to preserve the tree structure by combining GNN with Gated Recurrent Unit to integrate the hierarchical and sequential neighborhood information on the tree structure to node representations. The relational metric learning module aims to preserve the heterogeneity by embedding each type of nodes into a type-specific space with distinct distribution based on similarity metrics.
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。
