Article
Mathematics, Interdisciplinary Applications
Jiayun Wu, Langzhou He, Tao Jia, Li Tao
Summary: In this paper, a high-accuracy white-box TLP algorithm called DMAB is proposed by shifting the perspective of link prediction to the microscopic level of nodes. Two dynamic properties, node activity and node loyalty, are extracted and quantified to build the DMAB model. Comparative experiments with six state-of-the-art black-box methods on 12 real networks demonstrate that DMAB achieves excellent prediction performance and effectively captures network evolution mechanisms.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Mathematics, Interdisciplinary Applications
Chong Feng, Jianxu Ye, Jianlu Hu, Hui Lin Yuan
Summary: This paper presents a novel algorithm based on node betweenness properties, and experimental results demonstrate its effectiveness and superiority in community detection.
Article
Engineering, Electrical & Electronic
Axida Shan, Xiumei Fan, Celimuge Wu, Xinghui Zhang, Rui Men
Summary: Cooperative communication among nodes on roads is crucial in vehicular networks, but selfish nodes greatly reduce network performance. Therefore, this paper proposes a novel detection scheme that takes into account link quality, mobility, and vehicle behaviors using fuzzy logic in a decentralized manner. Additionally, a perception module based on probabilistic calculations is deployed to improve detection decisions. Simulation results demonstrate that the proposed scheme outperforms conventional schemes in terms of Precision, Recall, and F1-score, with acceptable communication overhead.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Oguz Findik, Emrah Ozkaynak
Summary: Link prediction is crucial for forecasting future links in complex networks, with traditional methods often falling short due to limited consideration of node weighting. This study proposes a novel model based on node weighting, showing superior success rates compared to current technology methods.
Article
Computer Science, Artificial Intelligence
Cunlai Pu, Jie Li, Jian Wang, Tony Q. S. Quek
Summary: This paper investigates the distribution of node similarity and proposes a measure called common neighbor based similarity (CNS). By using the generating function, a general framework is developed to calculate the CNS distributions of node sets in different networks. The paper also explores the connection between node similarity distribution and link prediction, and provides analytical solutions for two evaluation metrics. Moreover, the paper utilizes similarity distributions to optimize link prediction.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Guillaume Salha-Galvan, Johannes F. Lutzeyer, George Dasoulas, Romain Hennequin, Michalis Vazirgiannis
Summary: This paper introduces a high-accuracy community-preserving message passing scheme and corresponding training and optimization strategies for improving both link prediction and community detection tasks. The effectiveness of the proposed method is validated through experiments.
Article
Environmental Studies
Mingxue Zhu, Xuanru Zhou, Hua Zhang, Lu Wang, Haoyu Sun
Summary: As an important non-metallic strategic mineral resource, boron plays a vital role in the development of national emerging strategic industries. The international demand and trade volume of boron ore are increasing yearly, with closer trade relations and higher efficiency. Turkey holds the absolute export initiative in the boron ore international trade, while China and the United States are major importers and European countries serve as important transit countries. The import competition of boron ore is intensifying and concentrated, mainly with Turkey as the common import source, and future competition may shift to Latin America and Africa.
Article
Computer Science, Artificial Intelligence
Adnan Zeb, Summaya Saif, Junde Chen, Anwar Ul Haq, Zhiguo Gong, Defu Zhang
Summary: This paper proposes a novel extension of graph convolutional networks (GCNs) called ComplexGCN, which combines the expressiveness of complex geometry with GCNs to improve the representation quality of knowledge graph components. The proposed model demonstrates enhanced performance compared to existing methods on link prediction tasks.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Theory & Methods
Anisha Kumari, Ranjan Kumar Behera, Bibudatta Sahoo, Satya Prakash Sahoo
Summary: This paper introduces a link prediction model called LP-CD to simulate network evolution. It leverages existing communities in the network for link prediction and uses global similarity measures to identify non-existing links in the future. Experimental results show that LP-CD outperforms other approaches for link prediction.
Article
Physics, Multidisciplinary
Zhongyuan Jiang, Xiaoke Tang, Yong Zeng, Jinku Li, Jianfeng Ma
Summary: This work introduces a link deception method from the attacker's perspective, aiming to enhance the prediction probability of given targets by adding a small number of new links. The research first defines the link deception process and proposes greedy and heuristic algorithms to efficiently achieve the deception goal.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Shunjie Yuan, Hefeng Zeng, Ziyang Zuo, Chao Wang
Summary: This study proposes an overlapping community detection method CDMG based on Graph Convolutional Networks (GCN) to maximize the Markov stability of community structure. Extensive experiments demonstrate the superiority of CDMG compared to other established community detection algorithms. The performance of CDMG can be further improved by utilizing the optimal Markov time, which is found using a trichotomy-based method based on the influence of Markov time on CDMG performance.
COMPUTER COMMUNICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Chaobo He, Junwei Cheng, Xiang Fei, Yu Weng, Yulong Zheng, Yong Tang
Summary: Link prediction in attributed networks is challenging due to the need to effectively utilize community structure and attribute information. In this paper, we propose a novel CPAGCN method that combines AGCN and MLP to tackle this task. CPAGCN outperforms several strong competitors in link prediction, as demonstrated by extensive experiments on six real-world attributed networks.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Mathematics
Attila Mester, Andrei Pop, Bogdan-Eduard-Madalin Mursa, Horea Grebla, Laura Diosan, Camelia Chira
Summary: Evaluation of important nodes in a network can be done through different centrality measures and community detection algorithms, providing overlapping results and complementary information on important nodes.
Article
Automation & Control Systems
Phu Pham, Loan T. T. Nguyen, Ngoc Thanh Nguyen, Witold Pedrycz, Unil Yun, Bay Vo
Summary: This study introduces a novel dynamic network representation learning model called ComGCN, which can effectively handle the link prediction problem. The model combines node embedding and intracommunity features, demonstrating superior performance compared to recent state-of-the-art baselines on real-world dynamic networks.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2022)
Article
Computer Science, Information Systems
Ming-Ren Chen, Ping Huang, Yu Lin, Shi-Min Cai
Summary: This study introduces a novel graph embedding model SSNE for link prediction in sparse networks, which transforms the adjacency matrix and maps it to obtain node representation for nodal similarity calculation and link prediction. Experimental results demonstrate that SSNE outperforms other models in sparse networks.
Article
Computer Science, Artificial Intelligence
Hao Yang, Min Wang, Zhengfei Yu, Hang Zhang, Jinshen Jiang, Yun Zhou
Summary: In this paper, a novel method called CSTTA is proposed for test time adaptation (TTA), which utilizes confidence-based optimization and sample reweighting to better utilize sample information. Extensive experiments demonstrate the effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Jin Liu, Ju-Sheng Mi, Dong-Yun Niu
Summary: This article focuses on a novel method for generating a canonical basis for decision implications based on object-induced operators (OE operators). The logic of decision implication based on OE operators is described, and a method for obtaining the canonical basis for decision implications is given. The completeness, nonredundancy, and optimality of the canonical basis are proven. Additionally, a method for generating true premises based on OE operators is proposed.
KNOWLEDGE-BASED SYSTEMS
(2024)
Review
Computer Science, Artificial Intelligence
Kun Bu, Yuanchao Liu, Xiaolong Ju
Summary: This paper discusses the importance of sentiment analysis and pre-trained models in natural language processing, and explores the application of prompt learning. The research shows that prompt learning is more suitable for sentiment analysis tasks and can achieve good performance.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xiangjun Cai, Dagang Li
Summary: This paper presents a new decomposition mechanism based on learned decomposition mapping. By using a neural network to learn the relationship between original time series and decomposed results, the repetitive computation overhead during rolling decomposition is relieved. Additionally, extended mapping and partial decomposition methods are proposed to alleviate boundary effects on prediction performance. Comparative studies demonstrate that the proposed method outperforms existing RDEMs in terms of operation speed and prediction accuracy.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xu Wu, Yang Liu, Jie Tian, Yuanpeng Li
Summary: This paper proposes a blockchain-based privacy-preserving trust management architecture, which adopts federated learning to train task-specific trust models and utilizes differential privacy to protect device privacy. In addition, a game theory-based incentive mechanism and a parallel consensus protocol are proposed to improve the accuracy of trust computing and the efficiency of consensus.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zaiyang Yu, Prayag Tiwari, Luyang Hou, Lusi Li, Weijun Li, Limin Jiang, Xin Ning
Summary: This study introduces a 3D view-based approach that effectively handles occlusions and leverages the geometric information of 3D objects. The proposed method achieves state-of-the-art results on occluded ReID tasks and exhibits competitive performance on holistic ReID tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Yongliang Shi, Runyi Yang, Zirui Wu, Pengfei Li, Caiyun Liu, Hao Zhao, Guyue Zhou
Summary: Neural implicit representations have gained attention due to their expressive, continuous, and compact properties. However, there is still a lack of research on city-scale continual implicit dense mapping based on sparse LiDAR input. In this study, a city-scale continual neural mapping system with a panoptic representation is developed, incorporating environment-level and instance-level modeling. A tailored three-layer sampling strategy and category-specific prior are proposed to address the challenges of representing geometric information in city-scale space and achieving high fidelity mapping of instances under incomplete observation.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ruihan Hu, Zhi-Ri Tang, Rui Yang, Zhongjie Wang
Summary: Mesh data is crucial for 3D computer vision applications worldwide, but traditional deep learning frameworks have struggled with handling meshes. This paper proposes MDSSN, a simple mesh computation framework that models triangle meshes and represents their shape using face-based and edge-based Riemannian graphs. The framework incorporates end-to-end operators inspired by traditional deep learning frameworks, and includes dedicated modules for addressing challenges in mesh classification and segmentation tasks. Experimental results demonstrate that MDSSN outperforms other state-of-the-art approaches.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Buliao Huang, Yunhui Zhu, Muhammad Usman, Huanhuan Chen
Summary: This paper proposes a novel semi-supervised conditional normalizing flow (SSCFlow) algorithm that combines unsupervised imputation and supervised classification. By estimating the conditional distribution of incomplete instances, SSCFlow facilitates imputation and classification simultaneously, addressing the issue of separated tasks ignoring data distribution and label information in traditional methods.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Deeksha Varshney, Asif Ekbal, Erik Cambria
Summary: This paper focuses on the neural-based interactive dialogue system that aims to engage and retain humans in long-lasting conversations. It proposes a new neural generative model that combines step-wise co-attention, self-attention-based transformer network, and an emotion classifier to control emotion and knowledge transfer during response generation. The results from quantitative, qualitative, and human evaluation show that the proposed models can generate natural and coherent sentences, capturing essential facts with significant improvement over emotional content.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Junchen Ye, Weimiao Li, Zhixin Zhang, Tongyu Zhu, Leilei Sun, Bowen Du
Summary: Modeling multivariate time series has long been a topic of interest for scholars in various fields. This paper introduces MvTS, an open library based on Pytorch, which provides a unified framework for implementing and evaluating these models. Extensive experiments on public datasets demonstrate the effectiveness and universality of the models reproduced by MvTS.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Reham R. Mostafa, Ahmed M. Khedr, Zaher Al Aghbari, Imad Afyouni, Ibrahim Kamel, Naveed Ahmed
Summary: Feature selection is crucial in classification procedures, but it faces challenges in high-dimensional datasets. To overcome these challenges, this study proposes an Adaptive Hybrid-Mutated Differential Evolution method that incorporates the mechanics of the Spider Wasp Optimization algorithm and the concept of Enhanced Solution Quality. Experimental results demonstrate the effectiveness of the method in terms of accuracy and convergence speed, and it outperforms contemporary cutting-edge algorithms.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ti Xiang, Pin Lv, Liguo Sun, Yipu Yang, Jiuwu Hao
Summary: This paper introduces a Track Classification Model (TCM) based on marine radar, which can effectively recognize and classify shipping tracks. By using a feature extraction network with multi-feature fusion and a dataset production method to address missing labels, the classification accuracy is improved, resulting in successful engineering application in real scenarios.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zhihao Zhang, Yuan Zuo, Chenghua Lin, Junjie Wu
Summary: This paper proposes a novel unsupervised context-aware quality phrase mining framework called LMPhrase, which is built upon large pre-trained language models. The framework mines quality phrases as silver labels using a parameter-free probing technique on the pre-trained language model BERT, and formalizes the phrase tagging task as a sequence generation problem by fine-tuning on the Sequence to-Sequence pre-trained language model BART. The results of extensive experiments show that LMPhrase consistently outperforms existing competitors in two different granularity phrase mining tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Kemal Buyukkaya, M. Ozan Karsavuran, Cevdet Aykanat
Summary: The study aims to investigate the hybrid parallelization of the Stochastic Gradient Descent (SGD) algorithm for solving the matrix completion problem on a high-performance computing platform. A hybrid parallel decentralized SGD framework with asynchronous inter-process communication and a novel flexible partitioning scheme is proposed to achieve scalability up to hundreds of processors. Experimental results on real-world benchmark datasets show that the proposed algorithm achieves 6x higher throughput on sparse datasets compared to the state-of-the-art, while achieving comparable throughput on relatively dense datasets.
KNOWLEDGE-BASED SYSTEMS
(2024)
