Pub Date : 2024-11-16DOI: 10.1016/j.ins.2024.121660
Ziwei Wang , Jingtong Zhao , Jie Song
Social media platforms have gained increasing favor among advertisers as an effective way for product promotions. A key element is the dissemination of product reviews alongside advertisements. When a product enjoys an improving popularity, people are more tempted to make a purchase, which is called the “crowd effect.” In order to capture this effect, we use the number of “likes” as an indicator of the product's popularity and integrate it into the diffusion model as a global state accessible to all users. Our goal is to maximize overall influence within a finite time by selecting a fixed number of initial influencers. As the state evolves during the diffusion process, we demonstrate that the expected influence exhibits a non-submodular property, which brings difficulties to the problem. We develop an algorithm that generates candidate influencers with progressively refined estimation of the upper bound of popularity. This algorithm can automatically dig out important influencers and can be viewed as an intelligent decision support system. Through numerical experiments conducted on both random networks and two real networks, we illustrate the superiority of our method over several benchmark approaches, with an improvement up to 9.7%, highlighting the potential to enhance product promotions.
{"title":"Intelligent influencer selection in social networks for product promotions with crowd effect","authors":"Ziwei Wang , Jingtong Zhao , Jie Song","doi":"10.1016/j.ins.2024.121660","DOIUrl":"10.1016/j.ins.2024.121660","url":null,"abstract":"<div><div>Social media platforms have gained increasing favor among advertisers as an effective way for product promotions. A key element is the dissemination of product reviews alongside advertisements. When a product enjoys an improving popularity, people are more tempted to make a purchase, which is called the “crowd effect.” In order to capture this effect, we use the number of “likes” as an indicator of the product's popularity and integrate it into the diffusion model as a global state accessible to all users. Our goal is to maximize overall influence within a finite time by selecting a fixed number of initial influencers. As the state evolves during the diffusion process, we demonstrate that the expected influence exhibits a non-submodular property, which brings difficulties to the problem. We develop an algorithm that generates candidate influencers with progressively refined estimation of the upper bound of popularity. This algorithm can automatically dig out important influencers and can be viewed as an intelligent decision support system. Through numerical experiments conducted on both random networks and two real networks, we illustrate the superiority of our method over several benchmark approaches, with an improvement up to 9.7%, highlighting the potential to enhance product promotions.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"693 ","pages":"Article 121660"},"PeriodicalIF":8.1,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706183","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}
Pub Date : 2024-11-16DOI: 10.1016/j.ins.2024.121663
Jie Yan , Jing Liu , Yun Chen , Tao You , Xiao-Ke Ma , Zhong-Yuan Zhang
Clustering algorithms play a crucial role in various domains, and recent advancements in Generative Adversarial Network (GAN) techniques have opened new possibilities for improving clustering effectiveness. This paper aims to enhance the performance of GAN clustering by addressing the challenge of generating high-quality labeled samples. We propose a novel contrastive network and a voting-based method to progressively filter and fuse information from synthetic samples. These methods are incorporated into a deep clustering ensemble framework, which combines the advantages of GAN clustering and ensemble learning. Through comprehensive empirical analysis on diverse datasets, including both image and non-image datasets, we demonstrate the superiority of our proposed method in terms of effectiveness and robustness. Our approach outperforms existing GAN clustering methods while maintaining a reasonable computational time. This work contributes to the field of clustering algorithms by providing a more effective and robust approach for leveraging GANs in the clustering process. The code is available at https://github.com/Jarvisyan/CCEGAN-pytorch.
聚类算法在各个领域都发挥着至关重要的作用，而生成对抗网络（GAN）技术的最新进展为提高聚类效果提供了新的可能性。本文旨在通过解决生成高质量标记样本的难题来提高 GAN 聚类的性能。我们提出了一种新颖的对比网络和基于投票的方法，以逐步过滤和融合合成样本的信息。这些方法被纳入深度聚类集合框架，结合了 GAN 聚类和集合学习的优势。通过对各种数据集（包括图像和非图像数据集）进行全面的实证分析，我们证明了我们提出的方法在有效性和鲁棒性方面的优越性。在保持合理计算时间的同时，我们的方法优于现有的 GAN 聚类方法。这项工作为在聚类过程中利用 GAN 提供了一种更有效、更稳健的方法，从而为聚类算法领域做出了贡献。代码见 https://github.com/Jarvisyan/CCEGAN-pytorch。
{"title":"CCEGAN: Enhancing GAN clustering through contrastive clustering ensemble","authors":"Jie Yan , Jing Liu , Yun Chen , Tao You , Xiao-Ke Ma , Zhong-Yuan Zhang","doi":"10.1016/j.ins.2024.121663","DOIUrl":"10.1016/j.ins.2024.121663","url":null,"abstract":"<div><div>Clustering algorithms play a crucial role in various domains, and recent advancements in Generative Adversarial Network (GAN) techniques have opened new possibilities for improving clustering effectiveness. This paper aims to enhance the performance of GAN clustering by addressing the challenge of generating high-quality labeled samples. We propose a novel contrastive network and a voting-based method to progressively filter and fuse information from synthetic samples. These methods are incorporated into a deep clustering ensemble framework, which combines the advantages of GAN clustering and ensemble learning. Through comprehensive empirical analysis on diverse datasets, including both image and non-image datasets, we demonstrate the superiority of our proposed method in terms of effectiveness and robustness. Our approach outperforms existing GAN clustering methods while maintaining a reasonable computational time. This work contributes to the field of clustering algorithms by providing a more effective and robust approach for leveraging GANs in the clustering process. The code is available at <span><span>https://github.com/Jarvisyan/CCEGAN-pytorch</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"692 ","pages":"Article 121663"},"PeriodicalIF":8.1,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704065","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}
Open relation extraction is a critical task in natural language processing aimed at automatically extracting relations between entities in open-domain corpora. Most existing systems focus on extracting binary relations (relations between two entities) while extracting more complex n-ary relations (involving more than two entities) remains a significant challenge. Additionally, many previous systems rely on hand-crafted patterns and natural language processing tools, which result in error accumulation and reduced accuracy. The current study proposes a novel approach to open n-ary relation extraction that leverages recent advancements in deep learning architectures. This approach addresses the limitations of existing open relation extraction systems, particularly their reliance on hand-crafted patterns and their focus on binary relations. It utilizes SpanBERT to capture relational patterns from text data directly and introduces entity embedding vectors to create distinct representations of entities within sentences. These vectors enhance the proposed system’s understanding of the entities within the input sentence, leading to more accurate relation extraction. Notably, the proposed system in the present study achieves an F1-score of 89.79 and 92.67 on the LSOIE-wiki and OpenIE4 datasets, outperforming the best existing models by over 12% and 10%, respectively. These results highlight the effectiveness of the proposed approach in addressing the challenges of open n-ary relation extraction.
{"title":"Addressing the challenges of open n-ary relation extraction with a deep learning-driven approach","authors":"Mitra Isaee, Afsaneh Fatemi, Mohammadali Nematbakhsh","doi":"10.1016/j.ins.2024.121643","DOIUrl":"10.1016/j.ins.2024.121643","url":null,"abstract":"<div><div>Open relation extraction is a critical task in natural language processing aimed at automatically extracting relations between entities in open-domain corpora. Most existing systems focus on extracting binary relations (relations between two entities) while extracting more complex n-ary relations (involving more than two entities) remains a significant challenge. Additionally, many previous systems rely on hand-crafted patterns and natural language processing tools, which result in error accumulation and reduced accuracy. The current study proposes a novel approach to open n-ary relation extraction that leverages recent advancements in deep learning architectures. This approach addresses the limitations of existing open relation extraction systems, particularly their reliance on hand-crafted patterns and their focus on binary relations. It utilizes SpanBERT to capture relational patterns from text data directly and introduces entity embedding vectors to create distinct representations of entities within sentences. These vectors enhance the proposed system’s understanding of the entities within the input sentence, leading to more accurate relation extraction. Notably, the proposed system in the present study achieves an F1-score of 89.79 and 92.67 on the LSOIE-wiki and OpenIE4 datasets, outperforming the best existing models by over 12% and 10%, respectively. These results highlight the effectiveness of the proposed approach in addressing the challenges of open n-ary relation extraction.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"692 ","pages":"Article 121643"},"PeriodicalIF":8.1,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704075","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}
Pub Date : 2024-11-16DOI: 10.1016/j.ins.2024.121654
Jinpei Liu , Tianqi Shui , Feifei Jin , Longlong Shao , Jiaming Zhu
Group decision-making refers to the collective decision-making of multiple decision-makers to solve a common problem. However, decision-makers typically exhibit different psychological attitudes and behaviors in the decision-making process, which leads to actual decisions deviating from the solution recommendations obtained by traditional quantitative methods. More importantly, most existing studies on consensus fail to simultaneously consider the internal process of consensus building and psychological behaviors of decision-makers. To address this gap, this paper proposes a novel group decision-making method using a minimum deviation distribution ranking model and fairness concern-based consensus building. First, a fairness utility function is established to compare evaluation information from different decision-makers capturing fairness concern between individuals. Then, an optimization model is developed to determine decision-makers’ weights. Additionally, a fairness-based consensus model is proposed, which considers decision-makers’ acceptable adjustment range to avoid excessive information loss. To derive the priority vector, we develop a ranking model based on minimum deviation distribution by controlling the deviation in the minimum range. Finally, we provide a numerical example and comparative analysis to evaluate the effectiveness and superiority of our method, and conduct a robustness test to verify the stability and reliability of the ranking model.
{"title":"Minimum deviation distribution ranking model and fairness concern-based consensus building for group decision-making","authors":"Jinpei Liu , Tianqi Shui , Feifei Jin , Longlong Shao , Jiaming Zhu","doi":"10.1016/j.ins.2024.121654","DOIUrl":"10.1016/j.ins.2024.121654","url":null,"abstract":"<div><div>Group decision-making refers to the collective decision-making of multiple decision-makers to solve a common problem. However, decision-makers typically exhibit different psychological attitudes and behaviors in the decision-making process, which leads to actual decisions deviating from the solution recommendations obtained by traditional quantitative methods. More importantly, most existing studies on consensus fail to simultaneously consider the internal process of consensus building and psychological behaviors of decision-makers. To address this gap, this paper proposes a novel group decision-making method using a minimum deviation distribution ranking model and fairness concern-based consensus building. First, a fairness utility function is established to compare evaluation information from different decision-makers capturing fairness concern between individuals. Then, an optimization model is developed to determine decision-makers’ weights. Additionally, a fairness-based consensus model is proposed, which considers decision-makers’ acceptable adjustment range to avoid excessive information loss. To derive the priority vector, we develop a ranking model based on minimum deviation distribution by controlling the deviation in the minimum range. Finally, we provide a numerical example and comparative analysis to evaluate the effectiveness and superiority of our method, and conduct a robustness test to verify the stability and reliability of the ranking model.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"692 ","pages":"Article 121654"},"PeriodicalIF":8.1,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142704079","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}
Pub Date : 2024-11-15DOI: 10.1016/j.ins.2024.121642
Peter Eades , Seokhee Hong , Giuseppe Liotta , Fabrizio Montecchiani , Martin Nöllenburg , Tommaso Piselli , Stephen Wismath
Motivated by the need for decision-making systems that avoid bias and discrimination, the concept of fairness recently gained traction in the broad field of artificial intelligence, stimulating new research also within the information visualization community. In this paper, we introduce a notion of fairness in network visualization, specifically for orthogonal and for straight-line drawings of graphs, two foundational paradigms in the field. We investigate the following research questions: (i) What is the price, in terms of global readability, of incorporating fairness constraints in graph drawings? (ii) How unfair is a graph drawing that does not optimize fairness as a primary objective? We present both theoretical and empirical results. In particular, we design and implement two optimization algorithms for multi-objective functions, one based on an ILP model for orthogonal drawings, and one based on gradient descent for straight-line drawings. In a nutshell, we experimentally show that it is possible to significantly increase the fairness of a drawing by paying a relatively small amount in terms of reduced global readability. Also, we present a use case in which we qualitatively evaluate our approach on a practical scenario.
{"title":"Introducing fairness in network visualization","authors":"Peter Eades , Seokhee Hong , Giuseppe Liotta , Fabrizio Montecchiani , Martin Nöllenburg , Tommaso Piselli , Stephen Wismath","doi":"10.1016/j.ins.2024.121642","DOIUrl":"10.1016/j.ins.2024.121642","url":null,"abstract":"<div><div>Motivated by the need for decision-making systems that avoid bias and discrimination, the concept of fairness recently gained traction in the broad field of artificial intelligence, stimulating new research also within the information visualization community. In this paper, we introduce a notion of fairness in network visualization, specifically for orthogonal and for straight-line drawings of graphs, two foundational paradigms in the field. We investigate the following research questions: (i) What is the price, in terms of global readability, of incorporating fairness constraints in graph drawings? (ii) How unfair is a graph drawing that does not optimize fairness as a primary objective? We present both theoretical and empirical results. In particular, we design and implement two optimization algorithms for multi-objective functions, one based on an ILP model for orthogonal drawings, and one based on gradient descent for straight-line drawings. In a nutshell, we experimentally show that it is possible to significantly increase the fairness of a drawing by paying a relatively small amount in terms of reduced global readability. Also, we present a use case in which we qualitatively evaluate our approach on a practical scenario.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"691 ","pages":"Article 121642"},"PeriodicalIF":8.1,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-15DOI: 10.1016/j.ins.2024.121645
Mingxin Wang , Guohui Zhou , Yong Deng
The refined belief structure provides a more comprehensive capability for handling uncertain information. As an emerging belief model, the random permutation set (RPS) can be viewed as a layer-2 belief structure. Unlike fuzzy belief structures, RPS achieves precision without complex computations, as sequential calculations are more manageable. Previous studies typically assume a fixed and equidistant model for weak propensities in RPS. However, in practice, RPSs from different sources often fail to satisfy this assumption. This paper proposes the Quantitative Propensity Matrix (QPM) to distinguish the relative importance of elements within an ordered focal set and introduces a fusion method to integrate weak propensities across various RPSs. The QPM also enables the transformation of permutation mass functions (Perms) into probability mass functions (ProbMFs) through weight allocations. Given the varying reliability of information sources, the proposed method is applied to multi-source information fusion and decision-making processes. Some numerical experiments demonstrate the effectiveness of the proposed approach.
{"title":"Uncertainty management with quantitative propensity matrix in random permutation set theory","authors":"Mingxin Wang , Guohui Zhou , Yong Deng","doi":"10.1016/j.ins.2024.121645","DOIUrl":"10.1016/j.ins.2024.121645","url":null,"abstract":"<div><div>The refined belief structure provides a more comprehensive capability for handling uncertain information. As an emerging belief model, the random permutation set (RPS) can be viewed as a layer-2 belief structure. Unlike fuzzy belief structures, RPS achieves precision without complex computations, as sequential calculations are more manageable. Previous studies typically assume a fixed and equidistant model for weak propensities in RPS. However, in practice, RPSs from different sources often fail to satisfy this assumption. This paper proposes the Quantitative Propensity Matrix (QPM) to distinguish the relative importance of elements within an ordered focal set and introduces a fusion method to integrate weak propensities across various RPSs. The QPM also enables the transformation of permutation mass functions (Perms) into probability mass functions (ProbMFs) through weight allocations. Given the varying reliability of information sources, the proposed method is applied to multi-source information fusion and decision-making processes. Some numerical experiments demonstrate the effectiveness of the proposed approach.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"692 ","pages":"Article 121645"},"PeriodicalIF":8.1,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703998","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}
Pub Date : 2024-11-15DOI: 10.1016/j.ins.2024.121646
Licheng Sun , Hongbin Ma , Zhentao Guo
In many real-world scenarios, tasks involve coordinating multiple agents, such as managing robot clusters, drone swarms, and autonomous vehicles. These tasks are commonly addressed using Multi-Agent Reinforcement Learning (MARL). However, existing MARL algorithms often lack foresight regarding the number and types of agents involved, requiring agents to generalize across various task configurations. This may lead to suboptimal performance due to underestimated action values and the selection of less effective joint policies. To address these challenges, we propose a novel multi-agent deep reinforcement learning framework, called multi-agent reinforcement learning framework based on adaptive grouping dynamic topological space (GDT). GDT utilizes a group mesh topology to interconnect the local action value functions of each agent, enabling effective coordination and knowledge sharing among agents. By computing three different interpretations of action value functions, GDT overcomes monotonicity constraints and derives more effective overall action value functions. Additionally, GDT groups agents with high similarity to facilitate parameter sharing, thereby enhancing knowledge transfer and generalization across different scenarios. Furthermore, GDT introduces a strategy regularization method for optimal exploration of multiple action spaces. This method assigns each agent an independent entropy temperature during exploration, enabling agents to efficiently explore potential actions and approximate total state values. Experimental results demonstrate that our approach, termed GDT, significantly outperforms state-of-the-art algorithms on Google Research Football (GRF) and the StarCraft Multi-Agent Challenge (SMAC). Particularly in SMAC tasks, GDT achieves a success rate of nearly 100% across almost all Hard Map and Super Hard Map scenarios. Additionally, we validate the effectiveness of our algorithm on Non-monotonic Matrix Games.
{"title":"GDT: Multi-agent reinforcement learning framework based on adaptive grouping dynamic topological space","authors":"Licheng Sun , Hongbin Ma , Zhentao Guo","doi":"10.1016/j.ins.2024.121646","DOIUrl":"10.1016/j.ins.2024.121646","url":null,"abstract":"<div><div>In many real-world scenarios, tasks involve coordinating multiple agents, such as managing robot clusters, drone swarms, and autonomous vehicles. These tasks are commonly addressed using Multi-Agent Reinforcement Learning (MARL). However, existing MARL algorithms often lack foresight regarding the number and types of agents involved, requiring agents to generalize across various task configurations. This may lead to suboptimal performance due to underestimated action values and the selection of less effective joint policies. To address these challenges, we propose a novel multi-agent deep reinforcement learning framework, called multi-agent reinforcement learning framework based on adaptive grouping dynamic topological space (GDT). GDT utilizes a group mesh topology to interconnect the local action value functions of each agent, enabling effective coordination and knowledge sharing among agents. By computing three different interpretations of action value functions, GDT overcomes monotonicity constraints and derives more effective overall action value functions. Additionally, GDT groups agents with high similarity to facilitate parameter sharing, thereby enhancing knowledge transfer and generalization across different scenarios. Furthermore, GDT introduces a strategy regularization method for optimal exploration of multiple action spaces. This method assigns each agent an independent entropy temperature during exploration, enabling agents to efficiently explore potential actions and approximate total state values. Experimental results demonstrate that our approach, termed GDT, significantly outperforms state-of-the-art algorithms on Google Research Football (GRF) and the StarCraft Multi-Agent Challenge (SMAC). Particularly in SMAC tasks, GDT achieves a success rate of nearly 100% across almost all Hard Map and Super Hard Map scenarios. Additionally, we validate the effectiveness of our algorithm on Non-monotonic Matrix Games.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"691 ","pages":"Article 121646"},"PeriodicalIF":8.1,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658131","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}
Pub Date : 2024-11-15DOI: 10.1016/j.ins.2024.121647
Jie Mi , Huaiqin Wu , Jinde Cao
This article is concerned with the finite-time secure synchronization (FNTS) in mean square for stochastic complex networks (SCNs) with time-varying delayed coupling under deception attacks, where attack is described by a Bernoulli's stochastic variable, and is performed in the communication channel between the controller and the actuator. With the help of an auxiliary function, a new Halanay inequality is developed for continuous differential stochastic functions. By utilizing the Lyapunov functional gradient inequality with variable coefficients, a criterion about the finite-time stability in mean square is established for nonlinear stochastic systems under the designed two-step attenuation scheme. In order to reduce controller update consumption and communication waste, a two-step switching control mechanism consisting of an event-triggered control (ETC) and a time-varying gain state feedback control, is devised to achieve the FNTS objective. By Lyapunov stability theory, inequality analysis technique and the proposed finite-time stability criterion, the finite-time synchronization conditions are addressed in terms of linear matrix inequality (LMIs), and the bound of stochastic settling time (SST) is estimated explicitly. Finally, a practical application example is given to illustrate the effectiveness of the proposed control scheme, and to verify the correctness of the analytical results.
{"title":"Finite-time secure synchronization for stochastic complex networks with delayed coupling under deception attacks: A two-step switching control scheme","authors":"Jie Mi , Huaiqin Wu , Jinde Cao","doi":"10.1016/j.ins.2024.121647","DOIUrl":"10.1016/j.ins.2024.121647","url":null,"abstract":"<div><div>This article is concerned with the finite-time secure synchronization (FNTS) in mean square for stochastic complex networks (SCNs) with time-varying delayed coupling under deception attacks, where attack is described by a Bernoulli's stochastic variable, and is performed in the communication channel between the controller and the actuator. With the help of an auxiliary function, a new Halanay inequality is developed for continuous differential stochastic functions. By utilizing the Lyapunov functional gradient inequality with variable coefficients, a criterion about the finite-time stability in mean square is established for nonlinear stochastic systems under the designed two-step attenuation scheme. In order to reduce controller update consumption and communication waste, a two-step switching control mechanism consisting of an event-triggered control (ETC) and a time-varying gain state feedback control, is devised to achieve the FNTS objective. By Lyapunov stability theory, inequality analysis technique and the proposed finite-time stability criterion, the finite-time synchronization conditions are addressed in terms of linear matrix inequality (LMIs), and the bound of stochastic settling time (SST) is estimated explicitly. Finally, a practical application example is given to illustrate the effectiveness of the proposed control scheme, and to verify the correctness of the analytical results.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"691 ","pages":"Article 121647"},"PeriodicalIF":8.1,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658128","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}
Pub Date : 2024-11-15DOI: 10.1016/j.ins.2024.121649
Zengyou He , Xiaolei Li , Lianyu Hu , Mudi Jiang , Yan Liu
The detection of communities from a graph is a key issue in network science and graph data mining. However, existing community detection algorithms can always partition a given network/graph into different communities/subgraphs, even when no community structure exists. Obviously, it will lead to fruitless efforts and erroneous conclusions if we conduct the community detection procedure on a network without a community structure. Hence, prior to community detection, it is a must to test whether the community structure is present in the target network. Unfortunately, the community structure testing issue is still not revolved and existing solutions have some limitations. Therefore, we present a new test, which is called FCN (Frequent Common Neighbor) test to tackle the community structure testing problem. In FCN test, the number of FCN sets is employed as the test statistic, which will approximately follows a Poisson distribution when the support threshold is sufficiently large under the null hypothesis that the graph is generated according to the Erdős-Rényi model. We compare the proposed FCN test with existing community structure testing methods on both real networks and simulated networks. The experimental results demonstrate the effectiveness and advantage of our method.
{"title":"Community structure testing by counting frequent common neighbor sets","authors":"Zengyou He , Xiaolei Li , Lianyu Hu , Mudi Jiang , Yan Liu","doi":"10.1016/j.ins.2024.121649","DOIUrl":"10.1016/j.ins.2024.121649","url":null,"abstract":"<div><div>The detection of communities from a graph is a key issue in network science and graph data mining. However, existing community detection algorithms can always partition a given network/graph into different communities/subgraphs, even when no community structure exists. Obviously, it will lead to fruitless efforts and erroneous conclusions if we conduct the community detection procedure on a network without a community structure. Hence, prior to community detection, it is a must to test whether the community structure is present in the target network. Unfortunately, the community structure testing issue is still not revolved and existing solutions have some limitations. Therefore, we present a new test, which is called FCN (Frequent Common Neighbor) test to tackle the community structure testing problem. In FCN test, the number of FCN sets is employed as the test statistic, which will approximately follows a Poisson distribution when the support threshold is sufficiently large under the null hypothesis that the graph is generated according to the Erdős-Rényi model. We compare the proposed FCN test with existing community structure testing methods on both real networks and simulated networks. The experimental results demonstrate the effectiveness and advantage of our method.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"691 ","pages":"Article 121649"},"PeriodicalIF":8.1,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658127","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}
Pub Date : 2024-11-15DOI: 10.1016/j.ins.2024.121644
Kecan Cai , Hongyun Zhang , Miao Li , Duoqian Miao
Efficiency is crucial in deep learning tasks and has garnered significant attention in green deep learning research field. However, existing methods often sacrifice efficiency for slight accuracy improvement, requiring extensive computational resources. This paper proposes an adaptive granular data compression and interval granulation method to improve classification efficiency without compromising accuracy. The approach comprises two main components: Adaptive Granular Data Compression (AG), and Interval Granulation (IG). Specifically, AG employs principle of justifiable granularity for adaptive generating granular data. AG enables the extraction of abstract granular subset representations from the original dataset, capturing essential features and thereby reducing computational complexity. The quality of the generated granular data is evaluated using coverage and specificity criteria, which are standard metrics in evaluating information granules. Furthermore, the design of IG performs AG operation on the input data at regular intervals during the training process. The multiple regular granulation operations during the training process increase the diversity of samples and help the model achieve better training. It is noteworthy that the proposed method can be extended to any convolution-based and attention-based classification neural network. Extensive experiments conducted on benchmark datasets demonstrate that the proposed method significantly enhances the classification efficiency without compromising accuracy.
效率在深度学习任务中至关重要，在绿色深度学习研究领域备受关注。然而，现有的方法往往牺牲效率来换取微小的准确率提升，这需要大量的计算资源。本文提出了一种自适应粒度数据压缩和区间粒度化方法，以在不影响准确性的前提下提高分类效率。该方法由两个主要部分组成：自适应粒度数据压缩（AG）和间隔粒化（IG）。具体来说，AG 采用合理粒度原则自适应生成粒度数据。AG 可以从原始数据集中提取抽象的粒度子集表示，捕捉基本特征，从而降低计算复杂度。生成的粒度数据的质量使用覆盖率和特异性标准进行评估，这两个标准是评估信息粒度的标准指标。此外，IG 的设计在训练过程中定期对输入数据执行 AG 操作。训练过程中的多次定时颗粒化操作增加了样本的多样性，有助于模型实现更好的训练效果。值得注意的是，所提出的方法可以扩展到任何基于卷积和注意力的分类神经网络。在基准数据集上进行的大量实验证明，所提出的方法能在不影响准确性的前提下显著提高分类效率。
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