ATTRIBUTES RECONSTRUCTION IN HETEROGENEOUS NETWORKS VIA GRAPH AUGMENTATION

Abstract

Heterogeneous Graph Neural Networks(HGNNs), as an effective tool for mining heterogeneous graphs, have achieved remarkable performance on node classification tasks. Yet, HGNNs are limited in their mining power as they require all nodes to have complete and reliable attributes. It is usually unrealistic since the attributes of many nodes in reality are inevitably missing or defective. Existing methods usually take imputation schemes to complete missing attributes, in which topology information is ignored, leading to suboptimal performance. And some graph augmentation techniques have improved the quality of attributes, while few of them are designed for heterogeneous graphs. In this work, we study the data augmentation on heterogeneous graphs, tackling the missing and defective attributes simultaneously, and propose a novel generic architecture-Attributes Reconstruction in Heterogeneous networks via Graph Augmentation(ARHGA), including random sampling, attribute augmentation and consistency training. In graph augmentation, to ensure attributes plausible and accurate, the attention mechanism is adopted to reconstruct attributes under the guidance of the topological relationship between nodes. Our proposed architecture can be easily combined with any GNN-based heterogeneous model, and improves the performance. Extensive experiments on three benchmark datasets demonstrate the superior performance of ARHGA over strate-of-the-art baselines on semi-supervised node classification.



)). Most HGNNs follow a message-passing scheme in which each node updates its embedding by aggregating information of its neighbors'attributes. Such message-passing scheme usually requires that all nodes have complete and reliable attributes, which is not always satisfied in practice due to resource limitation and personal privacy, resulting in missing and defective attributes. In general, the attribute missing in heterogeneous graphs means that attributes of partial nodes are entirely missing, compared to that in homogeneous graphs, is more frequent and complex. Take DBLP(Sun & Han (2013)) as an example, the network has four types of nodes(author, paper, term and venue) and three types of links. Only paper nodes have attributes which are extracted from the keywords in their titles, while other types of nodes have no attributes. It impairs the effectiveness of the corresponding graph mining model to certain extents. In another fold, the original attributes of nodes are sometimes not ideal since heterogeneous graphs are extracted from complex systems which inevitably are subject to various forms of contamination, such as mistakes and adversarial attacks, making error propagation and greatly affecting the process of message-passing. This suggests the need for effective approaches able to complete missing attributes and calibrate defective attributes in heterogeneous graphs simultaneously. To alleviate the effect incurred from missing attributes, the existing models usually adopt imputation strategy, such as neighbor's average or one-hot vector as done in MAGNN(Fu et al. (2020) ). These



networks(HINs)(Yang et al. (2020); Shi et al. (2016); Shen et al. (2017)), which contain multiple types of nodes and edges, have been widely used to model complex systems and solve practical problems. Recently, heterogeneous graph neural networks have emerged as prevalent deep learning architectures to analyze HINs and shown superior performance in various graph analytical tasks, such as node classification(Wang et al. (2019a); Yun et al. (2019)) and link prediction(Fu et al. (2020); Zhang et al. (

