## 1 Introduction
Although these consistency-based methods construct consistency constraints with clear assumptions, data augmentation and perturbation methods may not be fully reliable [17]. Graph Neural Network (GNN) and Autoencoder-based models fall under graph-based methods. However, conventional graph structures are inherently limited to modeling pairwise relationships, which may not fully capture the higher-order dependencies often present in complex real-world data. To address this limitation, Hypergraph-enhanced Dual Semi-supervised Graph Classification (HDSGC) has been proposed as a powerful alternative that incorporates both graph and hypergraph representations to better capture complex and high-order relationships among data points [20]. HDSGC utilizes hyperedges to represent multi-node interactions, enriching the contextual information available for learning. It introduces a dual semi-supervised learning strategy that jointly optimizes node classification on both graph and hypergraph views, leveraging the complementary strengths of each structure. This method achieves improved performance on graph classification tasks with limited labeled data by enhancing representation learning through multi-relational modeling. Although such methods learn more information with graphs, building relationship between training samples may not be always accurate [17]. Disagreement-based models and self-training models fall under pseudo-labeling methods. As an iterative self-training strategy, SemiEvol--Semi-supervised Fine-tuning for LLM Adaptation [20]--leverages both labeled and high-confidence pseudo-labeled data to progressively fine-tune Large Language Models (LLMs). By integrating teacher-student paradigms with dynamic data selection, SemiEvol facilitates robust adaptation across diverse tasks under semi-supervised conditions. Although these methods produce pseudo-labels for unlabeled examples using labeled examples, such pseudo-labels may become noisy [17]. Interpolation Consistency Training (ICT) [21], MixMatch [22], ReMixMatch [23], and FixMatch [24] are examples of hybrid methods. A hybrid SSL framework was introduced through Semi-supervised Active Learning for Graph-level Classification [25], which combines semi-supervised learning with active learning to efficiently label graph-structured data. It strategically selects the most informative graph instances to be labeled, thereby maximizing learning performance with minimal annotation cost. By leveraging both labeled and unlabeled graphs during training, the method balances exploration and exploitation to enhance graph-level classification. This approach demonstrates state-of-the-art results by integrating uncertainty estimation with structure-aware sampling strategies. Another hybrid SSL method was proposed by [26], which combines curriculum learning with pseudo-labeling to improve node classification in graph-based SSL. This method iteratively selects pseudo-labels for unlabeled nodes based on both confidence and the complexity of the node's features, forming a hybrid curriculum. This curriculum-guided strategy helps the model progressively learn from easier to harder instances, leading to better generalization and faster convergence. This approach outperforms traditional pseudo-labeling methods by incorporating a structured learning process that adapts to the evolving uncertainty of the model [26]. Although such hybrid methods offer more robustness and efficiency, the size of such methods is larger due to integration of various learning strategies. Moreover, based on output, SSL is of four types [27]: semi-supervised classification, semi-supervised clustering, semi-supervised dimension reduction, and semi-supervised regression. The continuous value of energy consumption of a building needs to be predicted [28]. However, among these four categories, semi-supervised regression research to predict value of a continuous variable is relatively scarce [29]. Prediction can be obtained in two ways: a data-driven approach and knowledge-driven approach [30].