Scholarly web is a vast network of knowledge connected by citations. However, this system is increasingly compromised by miscitation, where references do not support or even contradict the claims they are cited for. Current miscitation detection methods, which primarily rely on semantic similarity or network anomalies, struggle to capture the nuanced relationship between a citation's context and its place in the wider network. While large language models (LLMs) offer powerful capabilities in semantic reasoning for this task, their deployment is hindered by hallucination risks and high computational costs. In this work, we introduce LLM-Augmented Graph Learning-based Miscitation Detector (LAGMiD), a novel framework that leverages LLMs for deep semantic reasoning over citation graphs and distills this knowledge into graph neural networks (GNNs) for efficient and scalable miscitation detection. Specifically, LAGMiD introduces an evidence-chain reasoning mechanism, which uses chain-of-thought prompting, to perform multi-hop citation tracing and assess semantic fidelity. To reduce LLM inference costs, we design a knowledge distillation method aligning GNN embeddings with intermediate LLM reasoning states. A collaborative learning strategy further routes complex cases to the LLM while optimizing the GNN for structure-based generalization. Experiments on three real-world benchmarks show that LAGMiD achieves state-of-the-art miscitation detection with significantly reduced inference cost.

Abstract Scientific text classification is essential for efficiently organizing and assimilating scientific knowledge. However, existing methods struggle to classify ultra‐short scientific texts due to their limited content and complex hierarchical labeling. To overcome these challenges, we introduce the BERT‐HMCN framework, which combines Bidirectional Encoder Representations from Transformers (BERT) with a Hierarchical Multi‐label Classification Network (HMCN). This framework introduces a novel level‐fixed fine‐tuning strategy that strengthens the connection between text semantics and hierarchical labels, enhancing the representation of ultra‐short texts. We evaluated BERT‐HMCN's performance on a dataset of 75,065 program titles from the National Natural Science Foundation of China. Our results show that BERT‐HMCN outperforms existing models in both overall performance and hierarchical accuracy. We also conducted a comparative analysis with autoregressive large language models (LLMs), illustrating the strengths of each in different contexts. Further analysis confirms the effectiveness and robustness of the BERT‐HMCN framework. We discuss its theoretical contributions and practical applications, underscoring the broader implications of these results in scientific text classification and other related fields.

The class imbalance problem can cause classifiers to be biased toward the majority class and inclined to generate incorrect predictions. While existing studies have proposed numerous oversampling methods to alleviate class imbalance by generating extra minority class samples, these methods still have some inherent weaknesses and make the generated samples less informative. This study proposes a novel over-sampling method named the Expandable Borderline Smote (EB-Smote), which can address the weaknesses of existing over-sampling methods and generate more informative synthetic samples. In EB-Smote, not only minority class but also majority class is oversampled, and the synthetic samples are generated in the area between the selected minority and majority samples, which are close to the borderlines of their respective classes. EB-Smote can generate more informative samples by expanding the borderlines of minority and majority classes toward the actual decision boundary. Based on 27 imbalanced datasets and commonly used machine learning models, the experimental results demonstrate that EB-Smote significantly outperforms the other 8 existing oversampling methods. This study can provide theoretical guidance and practical recommendations to solve the crucial class imbalance problem in classification tasks.

Graph Neural Networks (GNNs) have gained attention for their ability in capturing node interactions to generate node representations. However, their performances are frequently restricted in real-world directed networks with natural hierarchical structures. Most current GNNs incorporate information from immediate neighbors or within predefined receptive fields, potentially overlooking long-range dependencies inherent in hierarchical structures. They also tend to neglect node adaptability, which varies based on their positions. To address these limitations, we propose a new model called Hierarchy-Aware Adaptive Graph Neural Network (HAGNN) to adaptively capture hierarchical long-range dependencies. Technically, HAGNN creates a hierarchical structure based on directional pair-wise node interactions, revealing underlying hierarchical relationships among nodes. The inferred hierarchy helps to identify certain key nodes, named Source Hubs in our research, which serve as hierarchical contexts for individual nodes. Shortcuts adaptively connect these Source Hubs with distant nodes, enabling efficient message passing for informative long-range interactions. Through comprehensive experiments across multiple datasets, our proposed model outperforms several baseline methods, thus establishing a new state-of-the-art in performance. Further analysis demonstrates the effectiveness of our approach in capturing relevant adaptive hierarchical contexts, leading to improved and explainable node representation.

This paper studies a variant of the Steiner tree problem in the Euclidean plane ℝ2: the minimum-number of a specific material for the full Steiner tree problem (MNFST, for short). This question is an extension of the Steiner tree problem and the full Steiner tree problem. It has a wide range of applications in real life. The MNFST has been shown to be NP-hard. In this paper, we propose two asymptotic polynomial-time approximation algorithms for this problem. These two algorithms satisfy OUT ≤ 2.428OPT + 1 and $OUT \leq 2.123sOPT + \frac{3}{2}$, respectively.

Risk measures play significant roles in determining the magnitude of risks. The traditional risk measures consider only the consequence (C) and the probability (P) and ignore the support of the knowledge behind to estimate C and P. Several researchers have suggested adding knowledge as a third dimension in the risk measures. However, the issues of how to embed the dimension of knowledge in the risk measures to output an explicit expression of the risk measure and how to measure the strength of knowledge remain unresolved. This paper proposes a new risk measure incorporating the dimension of knowledge, apart from C and P. It is shown that the proposed risk measure has the form of traditional risk measures when the risk assessor has full knowledge. In addition, a fuzzy multicriteria decision-making (MCDM) method is employed to assess the strength of knowledge. In the fuzzy MCDM method, an entropy optimization problem is solved to obtain fuzzy measures, which are critical for determining the score of the strength of knowledge. Finally, the proposed method is applied to a project risk assessment, showing the feasibility of the method.

The semisupervised least squares support vector machine (LS-S3VM) is an important enhancement of least squares support vector machines in semisupervised learning. Given that most data collected from the real world are without labels, semisupervised approaches are more applicable than standard supervised approaches. Although a few training methods for LS-S3VM exist, the problem of deriving the optimal decision hyperplane efficiently and effectually has not been solved. In this paper, a fully weighted model of LS-S3VM is proposed, and a simple integer programming (IP) model is introduced through an equivalent transformation to solve the model. Based on the distances between the unlabeled data and the decision hyperplane, a new indicator is designed to represent the possibility that the label of an unlabeled datum should be reversed in each iteration during training. Using the indicator, we construct an extended candidate set consisting of the indices of unlabeled data with high possibilities, which integrates more information from unlabeled data. Our algorithm is degenerated into a special scenario of the previous algorithm when the extended candidate set is reduced into a set with only one element. Two strategies are utilized to determine the descent directions based on the extended candidate set. Furthermore, we developed a novel method for locating a good starting point based on the properties of the equivalent IP model. Combined with the extended candidate set and the carefully computed starting point, a fast algorithm to solve LS-S3VM quasi-optimally is proposed. The choice of quasi-optimal solutions results in low computational cost and avoidance of overfitting. Experiments show that our algorithm equipped with the two designed strategies is more effective than other algorithms in at least one of the following three aspects: 1) computational complexity; 2) generalization ability; and 3) flexibility. However, our algorithm and other algorithms have similar levels of performance in the remaining aspects.