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Dual-level Mixup for Graph Few-shot Learning with Fewer Tasks

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arXiv 2025年

作者： Liu, Yonghao Li, Mengyu Giunchiglia, Fausto Huang, Lan Li, Ximing Feng, Xiaoyue Guan, Renchu College of Computer Science and Technology Jilin University Changchun China Department of Information Engineering and Computer Science University of Trento Trento Italy Key Laboratory of Symbolic Computation and Knowledge Engineering The Ministry of Education China

Graph neural networks have been demonstrated as a powerful paradigm for effectively learning graph-structured data on the web and mining content from it. Current leading graph models require a large number of labeled samples for training, which unavoidably leads to overfitting in few-shot scenarios. Recent research has sought to alleviate this issue by simultaneously leveraging graph learning and meta-learning paradigms. However, these graph meta-learning models assume the availability of numerous meta-training tasks to learn transferable meta-knowledge. Such assumption may not be feasible in the real world due to the difficulty of constructing tasks and the substantial costs involved. Therefore, we propose a SiMple yet effectIve approach for graph few-shot Learning with fEwer tasks, named SMILE. We introduce a dual-level mixup strategy, encompassing both within-task and across-task mixup, to simultaneously enrich the available nodes and tasks in meta-learning. Moreover, we explicitly leverage the prior information provided by the node degrees in the graph to encode expressive node representations. Theoretically, we demonstrate that SMILE can enhance the model generalization ability. Empirically, SMILE consistently outperforms other competitive models by a large margin across all evaluated datasets with in-domain and cross-domain settings. Our anonymous code can be found here. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

Dual-Network Cross-Learning for Metabolite-Disease Association Prediction

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IEEE Transactions on Computational Biology and Bioinformatic...

IEEE Transactions on computational Biology and Bioinformatics 2025年第2期22卷 545-556页

作者： Yanxin Chen Qiao Ning Yitong Zhang Hui Li Shikai Guo Department of Information Science and Technology Dalian Maritime University Dalian China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China Dalian Key Laboratory of Artificial Intelligence Dalian China

In recent years, increasing evidence has demonstrated a close association between metabolites and various complex human diseases, providing valuable insights for disease diagnosis, treatment, and prevention. Although deep learning-based approaches have achieved certain success in predicting metabolic disease associations, challenges remain in enriching graph information and effectively integrating metabolic and disease features. To address these issues, this paper proposes a model named DCMDA, which extracts deep features of both metabolites and diseases using Dual-network Cross-learning for Metabolite- Disease Association prediction. DCMDA consists of three parts. The data processing module integrates similarity networks with association networks to construct a heterogeneous network. The feature extraction module extracts features from the metabolite-disease association network based on the non-negative matrix factorization method and from the heterogeneous network using graph autoencoder techniques. The feature fusion module combines the association matrix feature with the heterogeneous network feature through a Cross-Attention mechanism, thereby obtaining deep representations of metabolites and diseases. These features are then used to train the model to predict association scores between metabolites and diseases. Experimental results demonstrate that in 5-fold cross-validation, DCMDA achieves an area under the receiver operating characteristic curve (AUC) of 97.8% and an area under the precision-recall curve (AUPR) of 97.9%, outperforming state-of-the-art prediction methods.

关键词： Diseases Feature extraction Data mining Semantics Deep learning Kernel Information entropy Prediction algorithms Noise Nearest neighbor methods

SMT(LIA) Sampling with High Diversity

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arXiv 2025年

作者： Lai, Yong Li, Junjie Luo, Chuan College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China School of Software Beihang University Beijing100191 China

Satisfiability Modulo Linear Integer Arithmetic, SMT(LIA) for short, is pivotal across various critical domains. Previous research has primarily focused on SMT solving techniques. However, in practical applications such as software and hardware testing, there is a need to generate a diverse set of solutions for use as test inputs. We have developed the first sampling framework that integrates local search with CDCL(T) techniques, named HighDiv, capable of generating a highly diverse set of solutions for constraints under linear integer theory. Initially, in the local search phase, we introduced a novel operator called boundary-aware movement. This operator performs random moves by considering the current state’s constraints on variables, thereby enhancing the diversity of variables during the search process. Furthermore, we have conducted an in-depth study of the preprocessing and variable initialization mechanisms within the framework, which significantly enhances the efficiency of subsequent local searches. Lastly, we use the solutions obtained from local search sampling as additional constraints to further explore the solution space using the stochastic CDCL(T) method. Experimental results demonstrate that HighDiv generates solutions with greater diversity compared to the state-of-the-art SMT(LIA) sampling tool, MeGASampler. Copyright © 2025, The Authors. All rights reserved.

关键词： Software testing

Boosting Short Text Classification with Multi-Source Information Exploration and Dual-Level Contrastive Learning

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arXiv 2025年

作者： Liu, Yonghao Li, Mengyu Pang, Wei Giunchiglia, Fausto Huang, Lan Feng, Xiaoyue Guan, Renchu Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education College of Computer Science and Technology Jilin University China Mathematical and Computer Sciences Heriot-Watt University United Kingdom University of Trento Italy

Short text classification, as a research subtopic in natural language processing, is more challenging due to its semantic sparsity and insufficient labeled samples in practical scenarios. We propose a novel model named MI-DELIGHT for short text classification in this work. Specifically, it first performs multi-source information (i.e., statistical information, linguistic information, and factual information) exploration to alleviate the sparsity issues. Then, the graph learning approach is adopted to learn the representation of short texts, which are presented in graph forms. Moreover, we introduce a dual-level (i.e., instance-level and cluster-level) contrastive learning auxiliary task to effectively capture different-grained contrastive information within massive unlabeled data. Meanwhile, previous models merely perform the main task and auxiliary tasks in parallel, without considering the relationship among tasks. Therefore, we introduce a hierarchical architecture to explicitly model the correlations between tasks. We conduct extensive experiments across various benchmark datasets, demonstrating that MI-DELIGHT significantly surpasses previous competitive models. It even outperforms popular large language models on several datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

Incomplete Graph Learning: A Comprehensive Survey

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arXiv 2025年

作者： Xia, Riting Liu, Huibo Li, Anchen Liu, Xueyan Zhang, Yan Zhang, Chunxu Yang, Bo College of Computer Science Inner Mongolia University Hohhot010021 China School of Computer Science and Technology Jilin University Jilin Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

Graph learning is a prevalent field that operates on ubiquitous graph data. Effective graph learning methods can extract valuable information from graphs. However, these methods are non-robust and affected by missing attributes in graphs, resulting in sub-optimal outcomes. This has led to the emergence of incomplete graph learning, which aims to process and learn from incomplete graphs to achieve more accurate and representative results. In this paper, we conducted a comprehensive review of the literature on incomplete graph learning. Initially, we categorize incomplete graphs and provide precise definitions of relevant concepts, terminologies, and techniques, thereby establishing a solid understanding for readers. Subsequently, we classify incomplete graph learning methods according to the types of incompleteness: (1) attribute-incomplete graph learning methods, (2) attribute-missing graph learning methods, and (3) hybrid-absent graph learning methods. By systematically classifying and summarizing incomplete graph learning methods, we highlight the commonalities and differences among existing approaches, aiding readers in selecting methods and laying the groundwork for further advancements. In addition, we summarize the datasets, incomplete processing modes, evaluation metrics, and application domains used by the current methods. Lastly, we discuss the current challenges and propose future directions for incomplete graph learning, with the aim of stimulating further innovations in this crucial field. To our knowledge, this is the first review dedicated to incomplete graph learning, aiming to offer valuable insights for researchers in related fields.1 Copyright © 2025, The Authors. All rights reserved.

关键词： knowledge graph

NON-RADIATING ELASTIC SOURCES IN INHOMOGENEOUS ELASTIC MEDIA AT CORNERS WITH APPLICATIONS

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arXiv 2025年

作者： Diao, Huaian Geng, Yueran Tang, Ruixiang School of Mathematics Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China School of Mathematics Jilin University Changchun130012 China Department of Mathematics City University of Hong Kong Kowloon Hong Kong

This paper is concerned with non-radiating elastic sources in inhomogeneous elastic media. We demonstrate that the value of non-radiating elastic sources must vanish at convex corners of their support, provided the sources exhibit Hölder continuous regularity near the corner. Additionally, their gradient must satisfy intricate algebraic relationships with the angles defining the underlying corners, assuming the sources have C1,α regularity with α ∈ (0, 1) in the neighborhood of the corners. To perform microlocal analysis around the corners, we employ the so-called complex geometrical optics (CGO) solutions as test functions within a partial differential system. These characterizations of non-radiating elastic sources in inhomogeneous elastic media at corners enable us to establish the unique identifiability results for determining the position and shape of radiating elastic sources by a single far-field measurement, both locally and globally. The uniqueness result by a single far-field measurement is a challenging problem with a colorful history in inverse scattering. Indeed, when the support of a radiating elastic source is a convex polygon, we can simultaneously determine the shape of the elastic source and its values at the corners, provided the source is Hölder continuous at the corner. Furthermore, when the source function exhibits C1,α regularity in the neighborhood of a corner, the gradient of the source function at that corner can also be generically *** Codes 35Q74, 35R30, 74B05, 86A22 © 2025, CC BY.

关键词： Geometrical optics

Sfmfold: A Multi-Modal Contrast Learning Model for Rna Secondary Structure Prediction

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SSRN

SSRN 2025年

作者： Li, Ang Liu, Yuanning Zhang, Zhaoyang Li, Shuwang Dong, Hao Lv, Meng College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Software Jilin University Changchun130012 China College of Information Engineering Chang’an University Xi’an710064 China

The secondary structure of RNA can regulate the interaction between RNA and protein and perform various biological tasks through specific folding shapes, which plays an important role in revealing biomolecular mechanisms and developing new vaccines and therapeutic drugs. However, existing RNA secondary structure prediction methods based on thermodynamics and traditional neural network models usually lack the ability to model long-distance dependencies of RNA molecules and cannot handle complex non-nested structures in RNA molecules. In this paper, we propose a multi-modal contrastive learning method SFMFold based on state space model, which captures long-distance interactions in RNA sequences through RNA sequence representation learning part, and models the structure of RNA molecules through RNA structure representation learning part. Finally, the features of the two modalities are aligned and deeply fused to obtain the final RNA molecule prediction map. We test SFMFold on several datasets, and the experiments show that our proposed model shows excellent performance in cross-family prediction and long sequence prediction tasks. In addition, SFMFold can also effectively predict non-nested structures such as pseudoknot with better accuracy than the SOTA methods. © 2025, The Authors. All rights reserved.

关键词： Prediction models

Scalable Precise computation of Shannon Entropy

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arXiv 2025年

作者： Lai, Yong Tong, Haolong Xu, Zhenghang Yin, Minghao College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China School of Computer Science and Information Technology Northeast Normal University Changchun130017 China

Quantitative information flow analyses (QIF) are a class of techniques for measuring the amount of confidential information leaked by a program to its public outputs. Shannon entropy is an important method to quantify the amount of leakage in QIF. This paper focuses on the programs modeled in Boolean constraints and optimizes the two stages of the Shannon entropy computation to implement a scalable precise tool PSE. In the first stage, we design a knowledge compilation language called ADD[∧] that combines Algebraic Decision Diagrams and conjunctive decomposition. ADD[∧] avoids enumerating possible outputs of a program and supports tractable entropy computation. In the second stage, we optimize the model counting queries that are used to compute the probabilities of outputs. We compare PSE with the state-of-the-art probably approximately correct tool EntropyEstimation, which was shown to significantly outperform the existing precise tools. The experimental results demonstrate that PSE solved 55 more benchmarks compared to EntropyEstimation in a total of 441. For 98% of the benchmarks that both PSE and EntropyEstimation solved, PSE is at least 10× as efficient as EntropyEstimation. Copyright © 2025, The Authors. All rights reserved.

关键词： Information leakage

DeepSelective: Feature Gating and Representation Matching for Interpretable Clinical Prediction

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arXiv 2025年

作者： Zhang, Ruochi Yang, Qian Wang, Xiaoyang Wu, Haoran Zhou, Qiong Wang, Yu Li, Kewei Wang, Yueying Fan, Yusi Zhang, Jiale Huang, Lan Liu, Chang Zhou, Fengfeng College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Beijing Life Science Academy Beijing102209 China Syneron Technology Guangzhou510700 China

The rapid accumulation of Electronic Health Records (EHRs) has transformed healthcare by providing valuable data that enhance clinical predictions and diagnoses. While conventional machine learning models have proven effective, they often lack robust representation learning and depend heavily on expert-crafted features. Although deep learning offers powerful solutions, it is often criticized for its lack of interpretability. To address these challenges, we propose DeepSelective, a novel end to end deep learning framework for predicting patient prognosis using EHR data, with a strong emphasis on enhancing model interpretability. DeepSelective combines data compression techniques with an innovative feature selection approach, integrating custom-designed modules that work together to improve both accuracy and interpretability. Our experiments demonstrate that DeepSelective not only enhances predictive accuracy but also significantly improves interpretability, making it a valuable tool for clinical decision-making. The source code is freely available at http://***/supp/*** . © 2025, CC BY-NC-ND.

关键词： Electronic health record