Graph Neural Networks (GNNs) are vulnerable to perturbations in both edges and attributes by fraudsters attempting to evade detection. A low-cost and effective perturbation strategy involves establishing connections with benign users and providing as little information as possible, leading to a graph with noisy structure and absent attributes. We formulate a novel problem as learning in Graphs with Noisy structures and Absent node attributes (LGNA), for which no existing methods are specifically designed. To mitigate this gap, we propose a reliable graph learning framework called RENA, which implements a “Dilution of Unreliable Information” approach for the LGNA task. The core principle of RENA is to utilize more reliable information to decrease the proportion of unreliable information, thus diluting its impact. Specifically, only the observed node attributes and unconnected node pairs are considered reliable, while imputed attributes and connected node pairs are deemed unreliable. We first randomly sample a large number of unconnected node pairs and fewer connected pairs to create different structural views to supervise structure learning and dilute the impact of noisy edges. Next, we apply a graph autoencoder framework, assigning higher weights to the observed attributes and lower weights to the imputed attributes during the reconstruction process, thereby diluting the impact of imputation noise. Experiments show that our method outperforms state-of-the-art baselines on LGNA scenarios and conventional incomplete graph learning tasks. Code is available at https://github.com/lxx01110/RENA.

Heterogeneous graphs (HGs), consisting of multiple types of nodes and links, can characterize a variety of real-world complex systems. Recently, heterogeneous graph neural networks (HGNNs), as a powerful graph embedding method to aggregate heterogeneous structure and attribute information, has earned a lot of attention. Despite the ability of HGNNs in capturing rich semantics which reveal different aspects of nodes, they still stay at a coarse-grained level which simply exploits structural characteristics. In fact, rich unstructured text content of nodes also carries latent but more fine-grained semantics arising from multi-facet topic-aware factors, which fundamentally manifest why nodes of different types would connect and form a specific heterogeneous structure. However, little effort has been devoted to factorizing them.

With the explosion of news information, personalized news recommendation has become very important for users to quickly find their interested contents. Most existing methods usually learn the representations of users and news from news contents for recommendation. However, they seldom consider high-order connectivity underlying the user-news interactions. Moreover, existing methods failed to disentangle a user’s latent preference factors which cause her clicks on different news. In this paper, we model the user-news interactions as a bipartite graph and propose a novel Graph Neural News Recommendation model with Unsupervised Preference Disentanglement, named GNUD. Our model can encode high-order relationships into user and news representations by information propagation along the graph. Furthermore, the learned representations are disentangled with latent preference factors by a neighborhood routing algorithm, which can enhance expressiveness and interpretability. A preference regularizer is also designed to force each disentangled subspace to independently reflect an isolated preference, improving the quality of the disentangled representations. Experimental results on real-world news datasets demonstrate that our proposed model can effectively improve the performance of news recommendation and outperform state-of-the-art news recommendation methods.