Article
Computer Science, Artificial Intelligence
Didier A. Vega-Oliveros, Liang Zhao, Anderson Rocha, Lilian Berton
Summary: Link prediction (LP) in networks is crucial for determining future interactions among elements in various domains. This study proposes a progressive-diffusion (PD) method based on nodes' propagation dynamics and introduces an evaluation metric considering both information diffusion capacity and LP accuracy. Experimental results demonstrate the effectiveness of the proposed method compared to prior art.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
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
Computer Science, Information Systems
Miaomiao Liu, Jingfeng Guo, Jing Chen, Yongsheng Zhang
Summary: A novel model was proposed for simultaneous link and sign prediction, with high accuracy for negative link prediction. Experiments showed the model's effectiveness on various datasets, achieving high prediction accuracy levels.
HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES
(2021)
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, Interdisciplinary Applications
Longjie Li, Hui Wang, Shiyu Fang, Na Shan, Xiaoyun Chen
Summary: This paper proposes a supervised similarity-based link prediction method, which measures the connection likelihood of a node pair by extracting structural features and searching for positive and negative k-nearest neighbors. Experimental results demonstrate that the proposed method outperforms other methods in terms of accuracy and stability.
INTERNATIONAL JOURNAL OF MODERN PHYSICS C
(2021)
Article
Chemistry, Multidisciplinary
Keping Li, Shuang Gu, Dongyang Yan
Summary: This paper introduces a link prediction method based on neural networks, which generates optimized networks with better network efficiency and global network structure reliability compared to traditional methods.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Yadan Luo, Zi Huang, Hongxu Chen, Yang Yang, Hongzhi Yin, Mahsa Baktashmotlagh
Summary: Signed link prediction in social networks aims to reveal the underlying relationships (i.e., links) among users (i.e., nodes) given their existing interactions. Existing graph-based approaches lack human-intelligible explanations for key questions, and thus a new framework, SIHG, is proposed. SIHG incorporates a signed attention module to identify representative neighboring nodes and preserve the geometry of antagonism. Extensive experiments demonstrate that SIHG outperforms existing methods in signed link prediction.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Information Systems
Yingjie Liu, Shihu Liu, Fusheng Yu, Xiyang Yang
Summary: This paper proposes a link prediction algorithm based on the initial information contribution of nodes. By quantifying the initial information contribution of nodes and analyzing the ways of information transmission between nodes, an effective link prediction algorithm is designed. Experimental results demonstrate its significant advantages in effectiveness and robustness, as well as its good performance in practical applications.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Hardware & Architecture
Rui Tang, Xingshu Chen, Chuancheng Wei, Qindong Li, Wenxian Wang, Haizhou Wang, Wei Wang
Summary: This paper proposes an interlayer link prediction framework based on multiple structural attributes (MulAtt) that calculates the matching degree of unmatched nodes once by leveraging the information of closed triad, intralayer links, matched neighbors, and intralayer links of neighbors simultaneously to ensure accuracy while reducing time consumption. The framework achieves better performance than several existing network structure-based methods in a non-iterative way.
Article
Computer Science, Interdisciplinary Applications
Feipeng Guo, Wei Zhou, Zifan Wang, Chunhua Ju, Shaobo Ji, Qibei Lu
Summary: Link prediction is a fundamental and key field in complex network research. This paper proposes a topological nearest-neighbors similarity method in a directed network to tackle the limitations of existing methods. The proposed method shows better performance in terms of lower error, higher accuracy, and stronger robustness through empirical validation on multiple real directed network datasets.
JOURNAL OF COMPUTATIONAL SCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Haoyi Fan, Fengbin Zhang, Yuxuan Wei, Zuoyong Li, Changqing Zou, Yue Gao, Qionghai Dai
Summary: This paper presents a method named HeteHG-VAE for link prediction in heterogeneous information networks. It maps a conventional HIN to a heterogeneous hypergraph with specific semantics to capture high-order semantics and complex relations among nodes, and learns deep latent representations of nodes and hyperedges from the heterogeneous hypergraph using a Bayesian deep generative framework. Experimental results demonstrate the effectiveness and efficiency of the proposed method.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Theory & Methods
Yang Tian, Gaofeng Nie, Hui Tian, Qimei Cui
Summary: This paper investigates the structural similarity based link prediction algorithms and proposes a method to enhance the performance of local information algorithms by utilizing different structure attributes of endpoints. Experimental results show that the node degree contributes the most to improving the algorithm performance.
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
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
Physics, Multidisciplinary
Herman Yuliansyah, Zulaiha Ali Othman, Azuraliza Abu Bakar
Summary: The cold-start problem occurs when a new user with limited information joins the network, making it challenging to predict new links in future networks. This study proposes a link prediction method, DGLP, enhanced by the gravity of node pairs inspired by Newton's law of gravity, to address the common neighbor's failure in predicting future relations for new users with cold-start problems.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2023)
Article
Pathology
Lingyun Wu, Mohammad Awaji, Sugandha Saxena, Michelle L. Varney, Bhawna Sharma, Rakesh K. Singh
AMERICAN JOURNAL OF PATHOLOGY
(2020)
Letter
Hematology
Lingyun Wu, Xiao Li, Feng Xu, Dong Wu, Qi He, Luxi Song, Chao Xiao, Youshan Zhao, Zheng Zhang, Juan Guo, Liyu Zhou, Jiying Su, Chunkang Chang
BRITISH JOURNAL OF HAEMATOLOGY
(2020)
Article
Biochemistry & Molecular Biology
Mohammad Awaji, Sugandha Saxena, Lingyun Wu, Dipakkumar R. Prajapati, Abhilasha Purohit, Michelle L. Varney, Sushil Kumar, Satyanarayana Rachagani, Quan P. Ly, Maneesh Jain, Surinder K. Batra, Rakesh K. Singh
Article
Cell Biology
Yuqian Zhu, Dandan Song, Juan Guo, Jiacheng Jin, Ying Tao, Zheng Zhang, Feng Xu, Qi He, Xiao Li, Chunkang Chang, Lingyun Wu
Summary: The study found that U2AF1 mutations are associated with poor prognosis in MDS and AML patients, significantly inhibiting cell proliferation and inducing cellular apoptosis in cell models. The results showed that U2AF1 mutations promoted FOXO3a-dependent apoptosis and NLRP3 inflammasome activation, leading to pyroptotic cell death. FOXO3a was identified as the key molecule on which these pathways converge.
CELL DEATH & DISEASE
(2021)
Article
Oncology
Xiao Li, Feng Xu, Zheng Zhang, Juan Guo, Qi He, Lu-Xi Song, Dong Wu, Li-Yu Zhou, Ji-Ying Su, Chao Xiao, Chun-Kang Chang, Ling-Yun Wu
Summary: BCOR mutations are more common in CN MDS patients, predicting a higher risk of leukemia transformation. BCORMUT patients showed a better response to decitabine and achieved longer post-CR survival.
CLINICAL EPIGENETICS
(2021)
Article
Multidisciplinary Sciences
Bo Gao, Yue Zhao, Yonghang Gao, Guojun Li, Ling-Yun Wu
Summary: High-throughput biological data has provided an opportunity to illuminate the mechanisms of tumor emergence and evolution, with the tool ComCovEx being developed to explore common cancer driver gene modules between two cancers and reveal their associations. The research results offer new insights into the pathological basis of different cancer types and provide new clues for the diagnosis and treatment of associated cancers.
Review
Operations Research & Management Science
Xu Wang, Ling-Yun Wu
Summary: Blockchain technology has rapidly developed and been applied in various scenarios beyond cryptocurrencies, with a focus on security, efficiency, and resource allocation. This paper aims to analyze blockchain technology from the perspective of operations research and address relevant issues to promote its wider application in the future.
JOURNAL OF THE OPERATIONS RESEARCH SOCIETY OF CHINA
(2022)
Article
Biochemical Research Methods
Jiacheng Leng, Ling-Yun Wu
Summary: This study introduces a novel Importance-Penalized Joint Graphical Lasso method (IPJGL) for differential network inference, where the importance of genes is taken into consideration. The method is validated through simulation experiments and real datasets. Additionally, a new metric named APC2 is proposed to assess the differential levels of gene pairs.
Article
Biotechnology & Applied Microbiology
Duanchen Sun, Xiangnan Guan, Amy E. Moran, Ling-Yun Wu, David Z. Qian, Pepper Schedin, Mu-Shui Dai, Alexey Danilov, Joshi J. Alumkal, Andrew C. Adey, Paul T. Spellman, Zheng Xia
Summary: Scissor is a method that identifies cell subpopulations associated with a given phenotype from single-cell data, by quantifying the similarity between single cells and bulk samples, and optimizing a regression model with sample phenotype. Applied in lung cancer, melanoma, facioscapulohumeral muscular dystrophy, and Alzheimer's disease datasets, Scissor effectively identifies biologically and clinically relevant cell subpopulations.
NATURE BIOTECHNOLOGY
(2022)
Article
Biochemical Research Methods
Jiacheng Leng, Ling-Yun Wu
Summary: Gene-based transcriptome analysis can identify key factors causing disease production and cell differentiation, but basic life activities are mainly driven by gene interactions, requiring interaction-based transcriptome analysis to investigate insights into differential gene interactions.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Physics, Multidisciplinary
Jiating Yu, Jiacheng Leng, Duanchen Sun, Ling-Yun Wu
Summary: Network models are widely used in various fields for their ability to represent relationships between variables. Network structure can be unclear due to factors like experimental noise and missing data, hindering downstream analyses such as community detection. Therefore, network denoising is necessary before analysis. However, the importance of network pre-processing for community detection has been neglected. In this study, a novel network denoising method, called Network Refinement (NR), was proposed to enhance the self-organization properties of complex networks through a global diffusion process. NR significantly improved the clarity of the network's mesoscale structure and boosted the performance of various community detection algorithms.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2023)
Article
Hematology
Yuqian Zhu, Lingyun Wu
Article
Physics, Multidisciplinary
Xiaoyu Shi, Jian Zhang, Xia Jiang, Juan Chen, Wei Hao, Bo Wang
Summary: This study presents a novel framework using offline reinforcement learning to improve energy consumption in road transportation. By leveraging real-world human driving trajectories, the proposed method achieves significant improvements in energy consumption. The offline learning approach demonstrates generalizability across different scenarios.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Junhyuk Woo, Soon Ho Kim, Hyeongmo Kim, Kyungreem Han
Summary: Reservoir computing (RC) is a new machine-learning framework that uses an abstract neural network model to process information from complex dynamical systems. This study investigates the neuronal and network dynamics of liquid state machines (LSMs) using numerical simulations and classification tasks. The findings suggest that the computational performance of LSMs is closely related to the dynamic range, with a larger dynamic range resulting in higher performance.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Yuwei Yang, Zhuoxuan Li, Jun Chen, Zhiyuan Liu, Jinde Cao
Summary: This paper proposes an extreme learning machine (ELM) algorithm based on residual correction and Tent chaos sequence (TRELM-DROP) for accurate prediction of traffic flow. The algorithm reduces the impact of randomness in traffic flow through the Tent chaos strategy and residual correction method, and avoids weight optimization using the iterative method. A DROP strategy is introduced to improve the algorithm's ability to predict traffic flow under varying conditions.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Chengwei Dong, Min Yang, Lian Jia, Zirun Li
Summary: This work presents a novel three-dimensional system with multiple types of coexisting attractors, and investigates its dynamics using various methods. The mechanism of chaos emergence is explored, and the periodic orbits in the system are studied using the variational method. A symbolic coding method is successfully established to classify the short cycles. The flexibility and validity of the system are demonstrated through analogous circuit implementation. Various chaos-based applications are also presented to show the system's feasibility.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Viorel Badescu
Summary: This article discusses the maximum work extraction from confined particles energy, considering both reversible and irreversible processes. The results vary for different types of particles and conditions. The concept of exergy cannot be defined for particles that undergo spontaneous creation and annihilation. It is also noted that the Carnot efficiency is not applicable to the conversion of confined thermal radiation into work.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
P. M. Centres, D. J. Perez-Morelo, R. Guzman, L. Reinaudi, M. C. Gimenez
Summary: In this study, a phenomenological investigation of epidemic spread was conducted using a model of agent diffusion over a square region based on the SIR model. Two possible contagion mechanisms were considered, and it was observed that the number of secondary infections produced by an individual during its infectious period depended on various factors.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Zuan Jin, Minghui Ma, Shidong Liang, Hongguang Yao
Summary: This study proposes a differential variable speed limit (DVSL) control strategy considering lane assignment, which sets dynamic speed limits for each lane to attract vehicle lane-changing behaviors before the bottleneck and reduce the impact of traffic capacity drop. Experimental results show that the proposed DVSL control strategy can alleviate traffic congestion and improve efficiency.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Matthew Dicks, Andrew Paskaramoorthy, Tim Gebbie
Summary: In this study, we investigate the learning dynamics of a single reinforcement learning optimal execution trading agent when it interacts with an event-driven agent-based financial market model. The results show that the agents with smaller state spaces converge faster and are able to intuitively learn to trade using spread and volume states. The introduction of the learning agent has a robust impact on the moments of the model, except for the Hurst exponent, which decreases, and it can increase the micro-price volatility as trading volumes increase.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Zhouzhou Yao, Xianyu Wu, Yang Yang, Ning Li
Summary: This paper developed a cooperative lane-changing decision system based on digital technology and indirect reciprocity. By introducing image scoring and a Q-learning based reinforcement learning algorithm, drivers can continuously evaluate gains and adjust their strategies. The study shows that this decision system can improve driver cooperation and traffic efficiency, achieving over 50% cooperation probability under any connected vehicles penetration and traffic density, and reaching 100% cooperation probability under high penetration and medium to high traffic density.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Josephine Nanyondo, Henry Kasumba
Summary: This paper presents a multi-class Aw-Rascle (AR) model with area occupancy expressed in terms of vehicle class proportions. The qualitative properties of the proposed equilibrium velocity and the stability conditions of the model are established. The numerical results show the effect of proportional densities on the flow of vehicle classes, indicating the realism of the proposed model.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Oliver Smirnov
Summary: This study proposes a new method for simultaneously estimating the parameters of the 2D Ising model. The method solves a constrained optimization problem, where the objective function is a pseudo-log-likelihood and the constraint is the Hamiltonian of the external field. Monte Carlo simulations were conducted using models of different shapes and sizes to evaluate the performance of the method with and without the Hamiltonian constraint. The results demonstrate that the proposed estimation method yields lower variance across all model shapes and sizes compared to a simple pseudo-maximum likelihood.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Przemyslaw Chelminiak
Summary: The study investigates the first-passage properties of a non-linear diffusion equation with diffusivity dependent on the concentration/probability density through a power-law relationship. The survival probability and first-passage time distribution are determined based on the power-law exponent, and both exact and approximate expressions are derived, along with their asymptotic representations. The results pertain to diffusing particles that are either freely or harmonically trapped. The mean first-passage time is finite for the harmonically trapped particle, while it is divergent for the freely diffusing particle.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Hidemaro Suwa
Summary: The choice of transition kernel is crucial for the performance of the Markov chain Monte Carlo method. A one-parameter rejection control transition kernel is proposed, and it is shown that the rejection process plays a significant role in determining the sampling efficiency.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)
Article
Physics, Multidisciplinary
Xudong Wang, Yao Chen
Summary: This article investigates the joint influence of expanding medium and constant force on particle diffusion. By starting from the Langevin picture and introducing the effect of external force in two different ways, two models with different force terms are obtained. Detailed analysis and derivation yield the Fokker-Planck equations and moments for the two models. The sustained force behaves as a decoupled force, while the intermittent force changes the diffusion behavior with specific effects depending on the expanding rate of the medium.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2024)