Graph Neural Networks (GNNs) have demonstrated impressive success across a range of graph-based tasks. However, their performance in node classification typically relies on enough high-quality labeled data which are difficult to obtain in practice. Self-training emerges as a promising solution to tackle the issue of label scarcity. Most existing studies in this direction mainly rely on classification scores to explore high-confidence unlabeled samples. Nevertheless, these methods often lead to false positive samples, which hinders the capability of GNNs. To this end, we propose a simple yet effective Topology-Aware Graph Self-Training (TA-GST) method. Specifically, we first explore the origin of false positives in pseudo-labeled samples. We then design a topology-aware scoring method, which considers both the classification score and connectivity pattern to enhance the reliability of pseudo-labeled samples. Besides, we depart TA-GST from the traditional teacher-student pattern and simplify it in an end-to-end manner. Extensive experiments on seven real-world datasets demonstrate the effectiveness of our method.

Graph Structure Learning (GSL) aims to simultaneously enhance the original graph and the performance of Graph Neural Networks. However, existing GSL methods for node classification fail to consider neighborhood label dependencies during training, which limits their ability to refine the graph structure in an adaptive manner. Furthermore, the training of those methods lacks a proper schedule based on graph structure quality, thereby yielding suboptimal performance. To address these challenges, we propose a novel GSL framework for node classification, termed DuAl hypeRgraph-enhanced curricuLum-guided graph structure learnING for node classification (DARLING). It first introduces a graph structure curriculum module to effectively discriminate the suboptimal graph structures by examining both the distribution of neighborhood labels and the degree of nodes. Subsequently, a self-supervised dual hypergraph similarity learning module is proposed to capture higher-order neighborhood label dependencies. This is achieved via formulating a pre-training task that involves hyperedge batch-filling within the dual hypergraph of the input graph. The experimental results on six datasets demonstrate that the proposed DARLING outperforms eleven state-of-the-art methods significantly, in terms of effectiveness and robustness.

Graph Neural Networks (GNNs) have demonstrated remarkable success in various scenarios. However, their impressive performance is under the assumption of class balance (i.e., equal training sample distribution across various categories). Once trapped in the class-imbalanced issue, the GNN-based models typically under-represent the minority ones, resulting in decreased performance compared to balanced graphs. A promising solution is to balance the graph in a generative manner. However, the existing studies overlook the consistency between the synthesized sample and its corresponding class. Furthermore, the homophily assumption (i.e., like attracts like) undermines the topological diversity of graphs, thereby complicating the capability of models to capture the true distribution and boundaries of the categories. To this end, we propose aConsistency-Aware andLooseHomophily guided generative method for class-imbalanced graphs, namelyGraphCALH. Specifically, we design a consistency-aware feature synthesis method to balance the node- wise characteristics and the class- wise commonality for the synthesized samples. Moreover, we devise a loose homophily guided topology modeling method to enrich the topological diversity and simplify category boundaries. The experimental results on eleven class-imbalanced datasets demonstrate that the proposed GraphCALH outperforms ten state-of-the-art methods. The source code of this work will be uploaded to Github.

Class imbalance is a widespread problem in graph-structured data. The existing studies tailored for class-imbalanced graphs are typically categorized into generative and re-weighting methods. However, the former merely focuses on quantity balance rather than learning balance. The latter performs the fine-tuning in a majority-minority paradigm, overlooking the authentic-generative one. In fact, the collaboration of them is capable of relieving their respective limitations. To this end, we propose a Mutual-Guidance method for class-imbalanced graphs, namely GraphMuGu. Specifically, we first design an uncertainty-aware method to quantify the number of synthesized samples for each category. Furthermore, we devise a similarity-aware method to re-weight the importance of the authentic and generative samples. To the best our knowledge, the proposed GraphMuGu is the first try to incorporate the generative and re-weighting methods into a unified framework. The experimental results on five class-imbalanced datasets demonstrate the superiority of the proposed method. The source codes are available at https://github.com/ZZY-GraphMiningLab/GraphMuGu.