# Heterogeneous Graph Purification Network: Purifying Noisy Heterogeneity without Metapaths
## 1 Introduction  
In recent years, graph representation learning has become increasingly popular due to the universal ability of graph-structured data to model many real-world complex systems, such as citation networks [1], social networks [2], recommendation systems [3] and so on. Now Graph Neural Network (GNN) is recognized as the most advanced technique for graph representation learning following the framework of message passing. GNNs can effectively make use of both network structure and node attributes to generate high quality representations. Most of the previous GNNs [4; 5; 6] mainly focus on homogeneous graphs, wherein all nodes and edges are treated as the same type. However, in realistic scenarios, heterogeneous graphs which contain various types of nodes and edges are actually much more general and common. Lots of useful semantic and structural information the heterogeneity brings will be lost if we simply adopt homogeneous GNNs on heterogeneous graphs, which require specifically designed models. Various models have been proposed to make GNNs suitable for heterogeneous graphs. The majority of existed methods [2; 7] adopt metapaths, which are predefined semanticpatterns designed by human prior knowledge, to extract clean and useful information from the noisy and complex heterogeneity. These metapath-based models firstly aggregate neighbor features at the scope of each metapath rather than the original graph to generate semantic representations, and then fuse these semantic representations to generate the final node representations. These methods have good interpretability and are widely used in practice, but face some inherent flaws: (1) High demand while less generality. Metapaths not only require researchers to have specific domain knowledge, but also need to be redesigned for different datasets and applications. (2) Limited expressive power. The model's learning capability is upper-bounded by artificial metapaths, which is contrary to the original purpose of deep learning to learn from data automatically. Considering the shortcomings of metapaths, some works make attempts to design metapath-free methods, e.g., [8; 9]. These models aggregate messages from a node's local neighborhood like GNNs, and model heterogeneity via applying extra modules to embed semantic information based on type-specific weight parameters into propagated messages. However, since these models choose to take the original noisy graph as input, they meet some tricky problems that heterogeneous graphs often inevitably possess: Information Lacking (IL) and Information Confusion (IC). IL means most types of node suffer from missing attributes and labels. IC means some structures of the original graph are noise and even harmful to specific downstream tasks. Take the OGB-MAG [10] citation network dataset for example. It has four types of nodes: paper, author, field of study and institution while only paper nodes have initial attributes and labels. Thus, obtaining good representations for most nodes in a graph can be challenging due to limited self-information and direct supervision. In this particular task, the goal is to predict whether a paper node belongs to a conference or journal venue. The edge type \"affiliated with\" between authors and institutions may have a less positive relationship to this task. Incorporating information from the IC and IL may help to reduce the effects of heterogeneity. However, most existing heterogeneous GNNs simply aggregate the representations of different types of nodes together during the message passing process. This can lead to the loss of the original distribution of different types of node features and cause information over-mixing, which is a serious problem. Based on this analysis, we can explain the surprising finding in [11] that heterogeneous GNNs do not necessarily outperform homogeneous GNNs in a fair comparison.