The prevalent class imbalance in real-world graphs significantly affects the performance of Graph Neural Networks (GNNs). Existing methods for analyzing graph imbalance ignore the influence of minority nodes during the dynamic model training process, resulting in performance limitations. In this paper, we focus on minority class information during model training, identifying and defining the minority class forgetting phenomenon that exists in graph imbalanced method training processes. To address this issue, we propose Graph Imbalance Experience Replay(GIER) framework. On one hand, the method enhances the model's ability to mine minority node information in historical data, thereby achieving feature completion for minority class nodes. On the other hand, the proposed short-term confidence mechanism allows the model to adaptively calibrate the topological relationships in high-confidence nodes, thereby mitigating the model's tendency to propagate erroneous information about minority classes during training. GIER is a unified framework consisting of two synergistic components: Long-term Subgraph Memory (LSM) constructs multi-period feature-representative subgraphs to address distribution imbalance, and Short-term Confidence Calibration (SCC) dynamically reconstructs graph topology through degree-aware node selection and confidence-based filtering to address topological imbalance. The extensive experimental results demonstrate that GIER effectively improves the classification performance of GNNs on imbalanced graphs, achieving up to a 3.44% improvement in BAcc over the state-of-the-art, and is particularly effective in extreme scenarios with very small minority classes.

Class imbalance with node quantity or node topology challenges graph node classification in real-world, such as fake user identification and fraud finance detection in social networks. Current studies mitigate the detrimental effects of class imbalance on graph neural networks through data augmentation and weight adjustment. However, these methods generate unreliable data and lack global generalizability, which results in poor classification performance for class-imbalanced nodes. To tackle the above issues, we propose a Graph Diffusion Enhanced Hypergraph method (GraphDEH), which introduces a hypergraph to capture high-order data correlations, strengthening semantic relational space of nodes. Specifically, we design a generator to constructs reliable hyperedges, which mitigate insufficient learning of minority classes caused by node quantity imbalance. Furthermore, we design an enhancer that employs graph diffusion to evaluate global influence among nodes, which increases homogeneous information in neighborhoods of minority-class nodes for node topology imbalance. GraphDEH outperforms multiple benchmark methods in class-imbalanced node classification across four benchmark datasets.

This paper presents a novel WLAN-based indoor localization algorithm (i.e., HED) to combat the environmental dynamics by tolerating the sequence disorders caused by AP (access point) changes, while harvesting from the bursting number of available wireless resources. Via extensive real-world experiments lasting for over 6 months, we show the superiority of our HED algorithm in terms of accuracy and complexity over two state-of-the-art solutions that are also designed to resist the dynamics, i.e., FreeLoc and LCS (Longest Common Subsequences). Moreover, experimental results not only confirm the benefits brought by environmental dynamics, but also provide valuable investigations and hand-on experiences on the real-world localization system.