Graph neural networks (GNNs) have achieved state-of-the-art performance in various graph representation learning scenarios. However, when applied to graph data in real world, GNNs have encountered scalability issues. Existing GNNs often have high computational load in both training and inference stages, making them incapable of meeting the performance needs of large-scale scenarios with a large number of nodes. Although several studies on scalable GNNs have developed, they either merely improve GNNs with limited scalability or come at the expense of reduced effectiveness. Inspired by knowledge distillation's (KDs) achievement in preserving performances while balancing scalability in computer vision and natural language processing, we propose an enhanced scalable GNN via KD (KD-SGNN) to improve the scalability and effectiveness of GNNs. On the one hand, KD-SGNN adopts the idea of decoupled GNNs, which decouples feature transformation and feature propagation in GNNs and leverages preprocessing techniques to improve the scalability of GNNs. On the other hand, KD-SGNN proposes two KD mechanisms (i.e., soft-target (ST) distillation and shallow imitation (SI) distillation) to improve the expressiveness. The scalability and effectiveness of KD-SGNN are evaluated on multiple real datasets. Besides, the effectiveness of the proposed KD mechanisms is also verified through comprehensive analyses.

As special information carriers containing both structure and feature information, graphs are widely used in graph mining, e.g., Graph Neural Networks (GNNs). However, graph data are stored separately in multiple distributed parties in some practical scenarios, which may not be directly shared due to conflicts of interest. Hence, federated graph neural networks are proposed to address such data silo issues while preserving each party’s privacy (or client). Nevertheless, different graph data distributions of various parties, which is known as the statistical heterogeneity, may degrade the performance of naive federated learning algorithms like FedAvg. In this article, we propose FedEgo, a federated graph learning framework based on ego-graphs to tackle the challenges above, in which each client will train their local models while also contributing to the training of a global model. FedEgo applies GraphSAGE over ego-graphs to make full use of the structure information and utilizes Mixup for privacy concerns. To deal with the statistical heterogeneity, we integrate personalization into learning and propose an adaptive mixing coefficient strategy that enables clients to achieve their optimal personalization. Extensive experimental results and in-depth analysis demonstrate the effectiveness of FedEgo.

Graph convolutional networks (GCNs) have achieved great success in many applications and have caught significant attention in both academic and industrial domains. However, repeatedly employing graph convolutional layers would render the node embeddings indistinguishable. For the sake of avoiding oversmoothing, most GCN-based models are restricted in a shallow architecture. Therefore, the expressive power of these models is insufficient since they ignore information beyond local neighborhoods. Furthermore, existing methods either do not consider the semantics from high-order local structures or neglect the node homophily (i.e., node similarity), which severely limits the performance of the model. In this article, we take above problems into consideration and propose a novel Semantics and Homophily preserving Network Embedding (SHNE) model. In particular, SHNE leverages higher order connectivity patterns to capture structural semantics. To exploit node homophily, SHNE utilizes both structural and feature similarity to discover potential correlated neighbors for each node from the whole graph; thus, distant but informative nodes can also contribute to the model. Moreover, with the proposed dual-attention mechanisms, SHNE learns comprehensive embeddings with additional information from various semantic spaces. Furthermore, we also design a semantic regularizer to improve the quality of the combined representation. Extensive experiments demonstrate that SHNE outperforms state-of-the-art methods on benchmark datasets.

Graph Neural Networks (GNNs) have exhibited their powerful ability of tackling nontrivial problems on graphs. However, as an extension of deep learning models to graphs, GNNs are vulnerable to noise or adversarial attacks due to the underlying perturbations propagating in message passing scheme, which can affect the ultimate performances dramatically. Thus, it's vital to study a robust GNN framework to defend against various perturbations. In this paper, we propose a Robust Tensor Graph Convolutional Network (RT-GCN) model to improve the robustness. On the one hand, we utilize multi-view augmentation to reduce the augmentation variance and organize them as a third-order tensor, followed by the truncated T-SVD to capture the low-rankness of the multi-view augmented graph, which improves the robustness from the perspective of graph preprocessing. On the other hand, to effectively capture the inter-view and intra-view information on the multi-view augmented graph, we propose tensor GCN (TGCN) framework and analyze the mathematical relationship between TGCN and vanilla GCN, which improves the robustness from the perspective of model architecture. Extensive experimental results have verified the effectiveness of RT-GCN on various datasets, demonstrating the superiority to the state-of-the-art models on diverse adversarial attacks for graphs.

Graph contrastive representation learning aims to learn discriminative node representations by contrasting positive and negative samples. It helps models learn more generalized representations to achieve better performances on downstream tasks, which has aroused increasing research interest in recent years. Simultaneously, signed graphs consisting of both positive and negative links have become ubiquitous with the growing popularity of social media. However, existing works on graph contrastive representation learning are only proposed for unsigned graphs (containing only positive links) and it remains unexplored how they could be applied to signed graphs due to the distinct semantics and complex relations between positive and negative links. Therefore we propose a novel Signed Graph Contrastive Learning model (SGCL) to bridge this gap, which to the best of our knowledge is the first research to employ graph contrastive representation learning on signed graphs. Concretely, we design two types of graph augmentations specific to signed graphs based on a significant signed social theory, i.e., balance theory. Besides, inter-view and intra-view contrastive learning are proposed to learn discriminative node representations from perspectives of graph augmentations and signed structures respectively. Experimental results demonstrate the superiority of the proposed model over state-of-the-art methods on both real-world social datasets and online game datasets.