Information Sciences最新文献

One-class graph autoencoder: A new end-to-end, low-dimensional, and interpretable approach for node classification

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-05 DOI: 10.1016/j.ins.2025.122060

Marcos Paulo Silva Gôlo, José Gilberto Barbosa de Medeiros Junior, Diego Furtado Silva, Ricardo Marcondes Marcacini

{"title":"One-class graph autoencoder: A new end-to-end, low-dimensional, and interpretable approach for node classification","authors":"Marcos Paulo Silva Gôlo, José Gilberto Barbosa de Medeiros Junior, Diego Furtado Silva, Ricardo Marcondes Marcacini","doi":"10.1016/j.ins.2025.122060","DOIUrl":"10.1016/j.ins.2025.122060","url":null,"abstract":"<div><div>One-class learning (OCL) for graph neural networks (GNNs) comprises a set of techniques applied when real-world problems are modeled through graphs and have a single class of interest. These methods may employ a two-step strategy: first representing the graph and then classifying its nodes. End-to-end methods learn the node representations while classifying the nodes in OCL process. We highlight three main gaps in this literature: (i) non-customized representations for OCL; (ii) the lack of constraints on hypersphere learning; and (iii) the lack of interpretability. This paper presents <strong><u>O</u></strong>ne-c<strong><u>L</u></strong>ass <strong><u>G</u></strong>raph <strong><u>A</u></strong>utoencoder (OLGA), a new OCL for GNN approach. OLGA is an end-to-end method that learns low-dimensional representations for nodes while encapsulating interest nodes through a proposed and new hypersphere loss function. Furthermore, OLGA combines this new hypersphere loss with the graph autoencoder reconstruction loss to improve model learning. The reconstruction loss is a constraint to the sole use of the hypersphere loss that can bias the model to encapsulate all nodes. Finally, our low-dimensional representation makes the OLGA interpretable since we can visualize the representation learning at each epoch. OLGA achieved state-of-the-art results and outperformed six other methods with statistical significance while maintaining the learning process interpretability with its low-dimensional representations.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"708 ","pages":"Article 122060"},"PeriodicalIF":8.1,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Three-way decision-based reinforcement learning for container vertical scaling

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-05 DOI: 10.1016/j.ins.2025.122045

Chunmao Jiang , Guojun Mao , Bin Xie

{"title":"Three-way decision-based reinforcement learning for container vertical scaling","authors":"Chunmao Jiang , Guojun Mao , Bin Xie","doi":"10.1016/j.ins.2025.122045","DOIUrl":"10.1016/j.ins.2025.122045","url":null,"abstract":"<div><div>Container-based cloud computing requires efficient and adaptive resource management, particularly when making vertical scaling decisions. Traditional approaches often struggle with workload variability and lack flexibility when faced with uncertainties in workload patterns. This paper introduces a novel three-way decision-based reinforcement learning (TWD-RL) model for container vertical scaling. The TWD-RL model partitions the state space into positive, boundary, and negative regions based on confidence measures derived from historical data and current system states. This partitioning enables more nuanced scaling decisions: immediate scaling in high-confidence states, deferring decisions in uncertain states, and exploring in low-confidence states. We provide a theoretical analysis of the model's convergence properties and optimality conditions, thus establishing its mathematical foundation. Furthermore, we evaluate our model using real-world workload data from the Google Cloud Platform. The results demonstrate that TWD-RL significantly outperforms traditional Vertical Pod Autoscaler (VPA) approaches with respect to average response time, Service Level Agreement (SLA) violations, and resource utilization efficiency.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"708 ","pages":"Article 122045"},"PeriodicalIF":8.1,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Investigation of consensus for nonlinear time-varying multiagent systems via data-driven techniques

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-04 DOI: 10.1016/j.ins.2025.122052

Yuanshan Liu, Yude Xia

引用次数: 0

Dynamic futures portfolio strategy: A multi-criteria nested sequential three-state three-way decision model based on herd behavior

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-03-03 DOI: 10.1016/j.ins.2025.122043

Han Wang , Yanbing Ju , Yongxing Chang , Enrique Herrera-Viedma

{"title":"Dynamic futures portfolio strategy: A multi-criteria nested sequential three-state three-way decision model based on herd behavior","authors":"Han Wang , Yanbing Ju , Yongxing Chang , Enrique Herrera-Viedma","doi":"10.1016/j.ins.2025.122043","DOIUrl":"10.1016/j.ins.2025.122043","url":null,"abstract":"<div><div>The futures portfolio is a key tool for addressing market volatility and complexity in the financial markets. Traditional static strategies struggle to keep up with the rapidly shifting market sentiment and herd behavior, leading to delayed decision-making and risk management failures. To enhance investment efficiency and improve risk control, we propose a dynamic multi-criteria nested sequential three-state three-way decision (TS3WD) model based on herd behavior to identify and implement herd behaviors and optimize the futures portfolio strategy. Firstly, this paper proposes a method for determining optimistic and pessimistic conditional probabilities based on loss functions, deriving new TS3WD and simplified decision rules. Secondly, the herd behavior discrimination method is introduced to divide it into positive, neutral, and negative herd behaviors for holding futures contracts. Thirdly, four minimum adjustment optimization models for positive and negative herd behaviors under optimistic and pessimistic attitudes are constructed based on new decision rules, respectively, and a method based on the self-confidence principle for neutral herd behavior is presented, providing a quantitative model for implementing herd behaviors. Subsequently, a progressive dynamic algorithm based on a multi-criteria nested sequential TS3WD model is proposed to deduce the futures portfolio strategy, which dynamically identifies and adjusts loss functions to obtain the optimal futures investment behavior, forming a complete futures portfolio strategy. Finally, we apply the proposed method to solve the metal futures portfolio strategy in the Shanghai Futures Exchange, providing implications for investors in the futures market through sensitivity and comparative analyses.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"708 ","pages":"Article 122043"},"PeriodicalIF":8.1,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep core node information embedding on networks with missing edges for community detection

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-02-28 DOI: 10.1016/j.ins.2025.122039

Rong Fei , Yuxin Wan , Bo Hu , Aimin Li , Yingan Cui , Hailong Peng

{"title":"Deep core node information embedding on networks with missing edges for community detection","authors":"Rong Fei , Yuxin Wan , Bo Hu , Aimin Li , Yingan Cui , Hailong Peng","doi":"10.1016/j.ins.2025.122039","DOIUrl":"10.1016/j.ins.2025.122039","url":null,"abstract":"<div><div>The incomplete network is defined as the network with missing edges, which forms incomplete network topology by missing real information because of multiple-factor such as personal privacy security and threats, etc. Academic interest in incomplete network studies is increasing. Some methods solving community detection problem in the incomplete network, as link prediction, show low ACC or NMI. To address those, there is a need for approaches less affected by missing edges and easy to obtain communities. We propose a deep core node information embedding(DCNIE) algorithm on network with missing edges for community detection, aiming to obtain core node information rather than the influence of edges. First, by edge augmentation, the network with missing edges is integrated into complete networks. Second, the <em>k</em>-core algorithm is used to obtain core node information and build a similarity matrix, followed by an unsupervised deep method that implements network embedding to obtain a low-dimensional feature matrix. Finally, Gaussian mixture model is used for clustering to obtain the community division. We compare eleven state-of-the-art methods on eleven real networks by using eight evaluation metrics. Experiments demonstrate that DCNIE is superior in performance and efficiency while gaining accurate community division in incomplete network.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"707 ","pages":"Article 122039"},"PeriodicalIF":8.1,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interval-valued fuzzy predicates from labeled data: An approach to data classification and knowledge discovery

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-02-28 DOI: 10.1016/j.ins.2025.122033

Diego S. Comas , Gustavo J. Meschino , Virginia L. Ballarin

{"title":"Interval-valued fuzzy predicates from labeled data: An approach to data classification and knowledge discovery","authors":"Diego S. Comas , Gustavo J. Meschino , Virginia L. Ballarin","doi":"10.1016/j.ins.2025.122033","DOIUrl":"10.1016/j.ins.2025.122033","url":null,"abstract":"<div><div>Interpretable data classifiers play a significant role in providing transparency in the decision-making process by ensuring accountability and auditability, enhancing model understanding, and extracting new information that expands the field of knowledge in a discipline while effectively handling large datasets. This paper introduces the Type-2 Label-based Fuzzy Predicate Classification (T2-LFPC) method, in which interval-valued fuzzy predicates are used for interpretable data classification. The proposed approach begins by clustering the data within each class, associating clusters with collections of common attributes, and identifying class prototypes. Interval-valued membership functions and predicates are then derived from these prototypes, leading to the creation of an interpretable classifier. Empirical evaluations on 14 datasets, both public and synthetic, are presented to demonstrate the superior performance of T2-LFPC based on the accuracy and Jaccard index. The proposed method enables linguistic descriptions of classes, insight into attribute semantics, class property definitions, and an understanding of data space partitioning. This innovative approach enhances knowledge discovery by addressing the challenges posed by the complexity and size of modern datasets.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"707 ","pages":"Article 122033"},"PeriodicalIF":8.1,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrated heterogeneous graph and reinforcement learning enabled efficient scheduling for surface mount technology workshop

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-02-28 DOI: 10.1016/j.ins.2025.122023

Biao Zhang , Hongyan Sang , Chao Lu , Leilei Meng , Yanan Song , Xuchu Jiang

{"title":"Integrated heterogeneous graph and reinforcement learning enabled efficient scheduling for surface mount technology workshop","authors":"Biao Zhang , Hongyan Sang , Chao Lu , Leilei Meng , Yanan Song , Xuchu Jiang","doi":"10.1016/j.ins.2025.122023","DOIUrl":"10.1016/j.ins.2025.122023","url":null,"abstract":"<div><div>Timely scheduling is crucial for manufacturing workshops to adapt swiftly to changing conditions. This paper introduces a novel deep heterogeneous graph and reinforcement learning approach to address real-time challenges in a surface mount technology (SMT) workshop. The scheduling problem in SMT workshop can be modeled as a reconfigurable distributed flowshop group scheduling problem (RDFGSP), involving assignment of family operations to cells for their flows, sequencing of family operations on the cells, and sequencing of job operations in the family operations for their flows. By mapping the problem to a heterogeneous graph with distinct node and edge types, an end-to-end learning model is developed. The model integrates a heterogeneous graph neural network (HGNN) and sequential Q networks to effectively represent the key scheduling elements and the Markov decision-making process. HGNN is employed to extract meaningful features and representations from the heterogeneous graph. These representations are then fed into the sequential Q networks to select two cooperated actions to be taken. A weighted sum approach is proposed to provide more reasonable evaluation of the selected actions. Experimental comparisons with exact and heuristic methods from the literature demonstrate the superior performance and effectiveness of the proposed model.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"708 ","pages":"Article 122023"},"PeriodicalIF":8.1,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143549447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Computation of synchronous diagnosis bases of discrete-event systems

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-02-27 DOI: 10.1016/j.ins.2025.122031

Lucas N.R. Reis, Marcos V. Moreira

{"title":"Computation of synchronous diagnosis bases of discrete-event systems","authors":"Lucas N.R. Reis, Marcos V. Moreira","doi":"10.1016/j.ins.2025.122031","DOIUrl":"10.1016/j.ins.2025.122031","url":null,"abstract":"<div><div>Several works have been proposed to address fault diagnosis of Discrete-Event Systems (DES) considering different approaches and architectures. In the vast majority, the fault diagnoser is constructed based on the complete system model, which may have a huge number of states, due to the parallel composition of several modules. The implementation of diagnosers with a large number of states consumes a large amount of computer memory, and may become, in some cases, unfeasible. Recently, synchronous diagnosis of DES has been proposed, where state observers of fault-free models of system modules are used for fault diagnosis. The method provides a diagnoser that is not based on the composed plant model, which leads to a diagnoser with fewer states and transitions than the classical diagnoser. In the synchronous diagnosis approach, all the subsystem models are assumed to contribute to fault detection. However, in practice, certain subsystems may not provide useful information on fault occurrences, or redundant information may be available from other modules. Consequently, these redundant modules are not necessary in the synchronous diagnosis scheme and can be discarded, leading to reduced diagnosers. In this paper, we present a method for computing a synchronous diagnoser that uses only part of the subsystem models. It is also shown that the fault can be diagnosed using modules where the fault event is not even modeled. To do so, we present an algorithm for computing all the sets of modules that ensure the synchronous diagnosability of a DES. These sets are called synchronous diagnosis bases (SDB). We prove that the complexity of the problem of finding an SDB with cardinality less than or equal to a given natural number is NP-complete. Thus, the algorithm proposed in this work has the objective of mitigating the computational efforts to find all the SDB of a DES. Two examples are used to illustrate the efficiency of the proposed method.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"707 ","pages":"Article 122031"},"PeriodicalIF":8.1,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed conflict analysis across varying analysis levels based on fuzzy formal contexts 基于模糊形式语境，在不同分析层面进行分布式冲突分析

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-02-27 DOI: 10.1016/j.ins.2025.122038

Zhenhao Qi , Huilai Zhi , Weiping Ding

{"title":"Distributed conflict analysis across varying analysis levels based on fuzzy formal contexts","authors":"Zhenhao Qi , Huilai Zhi , Weiping Ding","doi":"10.1016/j.ins.2025.122038","DOIUrl":"10.1016/j.ins.2025.122038","url":null,"abstract":"<div><div>Conflict analysis aims to understand the causes of conflicts and identify effective solutions. While existing studies have thoroughly examined conflict information within individual information systems, the conflict analysis across different systems remains underexplored. In this paper, we utilize fuzzy formal concept analysis to investigate conflict analysis in multi-source information. First, conflict information fusion strategies for distributed fuzzy formal contexts are proposed, including object set extension (vertical merging) and attribute set extension (horizontal merging). Second, algorithms for updating conflict analysis results when adjusting analysis levels are introduced, considering the varying conflict analysis levels inherent in multi-source information. Finally, the fusion strategies for conflict analysis results at varying analysis levels are evaluated, and the selection of analysis levels is analyzed to optimize computational efficiency. The experimental results demonstrate that fusing conflict information is significantly more efficient than recalculation, and it allows for the selection of varying analysis levels to balance time consumption and information volume. This work enhances the efficiency of conflict analysis in fuzzy formal contexts, providing practical methods for managing multi-source information and adjusting analysis levels to meet specific requirements.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"707 ","pages":"Article 122038"},"PeriodicalIF":8.1,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Information diffusion prediction via meta-knowledge learners

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-02-27 DOI: 10.1016/j.ins.2025.122034

Zhangtao Cheng , Jienan Zhang , Xovee Xu , Wenxin Tai , Fan Zhou , Goce Trajcevski , Ting Zhong

{"title":"Information diffusion prediction via meta-knowledge learners","authors":"Zhangtao Cheng , Jienan Zhang , Xovee Xu , Wenxin Tai , Fan Zhou , Goce Trajcevski , Ting Zhong","doi":"10.1016/j.ins.2025.122034","DOIUrl":"10.1016/j.ins.2025.122034","url":null,"abstract":"<div><div>Information diffusion prediction is a fundamental task for a vast range of applications, including viral marketing identification and precise recommendation. Existing works focus on modeling limited contextual information from independent cascades while overlooking the diverse user behaviors during the information diffusion: First, users typically have diverse social relationships and pay more attention to their social neighbors, which significantly influences the process of information diffusion. Second, complex temporal influence among different cascade sequences leads to unique and dynamic diffusion patterns between users. To tackle these challenges, we propose MetaCas, a novel cascade meta-knowledge learning framework for enhancing information diffusion prediction in an adaptive and dynamic parameter generative manner. Specifically, we design two meta-knowledge-aware topological-temporal modules – Meta-GAT and Meta-LSTM – to extract cascade-specific topological and temporal user interdependencies inherent within the information diffusion process. Model parameters of topological-temporal modules are adaptively generated by the constructed meta-knowledge from three important perspectives: user social structure, user preference, and temporal diffusion influence. Extensive experiments conducted on four real-world social datasets demonstrate that MetaCas outperforms state-of-the-art information diffusion models across several settings (up to 16.6% in terms of Hits@100).</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"707 ","pages":"Article 122034"},"PeriodicalIF":8.1,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0