Article
Computer Science, Artificial Intelligence
Jung Hyuk Seo, Myoung Ho Kim
Summary: This study introduces an I/O-efficient algorithm for large-scale graph data, pm-SCAN, capable of clustering structures even with limited memory, and proposes a cluster maintenance method for dynamic graph data that shows significant performance improvement compared to traditional methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Alon Bartal, Gilad Ravid
Summary: This study uses large-scale data on social media user interactions to study complex human behavior, proposing an innovative algorithm to represent large unobtainable user relationship graphs. Results show that statistics and distributions of nodes in a large unobtainable graph can be well represented by a smaller graph, and identifying influencers in an unobtainable graph can be effectively done by analyzing a representative graph.
INFORMATION SCIENCES
(2021)
Article
Neurosciences
Meimei Liu, Zhengwu Zhang, David B. Dunson
Summary: This study introduces a nonlinear latent factor model called GATE, based on recent advances in deep learning, to explore the relationship between brain connectomes and cognition. The results show significant advantages of GATE over competitors in terms of prediction accuracy, statistical inference, and computing efficiency, revealing a stronger association between human cognitive traits and structural brain connectomes.
Article
Computer Science, Interdisciplinary Applications
Claudia von Bromssen, Staffan Betner, Jens Folster, Karin Eklof
Summary: Generalized additive models are increasingly utilized to identify and describe environmental trends, providing more precise estimates compared to simpler statistical tools, and requiring flexible visualization techniques for comprehensive analysis.
ENVIRONMENTAL MODELLING & SOFTWARE
(2021)
Article
Computer Science, Software Engineering
Bo Jiao, Xin Lu, Jingbo Xia, Brij Bhooshan Gupta, Lei Bao, Qingshan Zhou
Summary: This paper proposes a hierarchical structure model and a corresponding hierarchical structure sampling algorithm for sampling large-scale scale-free graphs in visualization. The algorithm can preserve the core community structure, important minority structures, and the connection relationship between low-degree nodes in the graph.
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
(2023)
Article
Computer Science, Artificial Intelligence
Weishuai Che, Zhaowei Liu, Yingjie Wang, Jinglei Liu
Summary: The development of the Internet and big data has led to the importance of graphs as a data representation structure. However, as data size increases, graph embedding faces challenges in computational complexity and memory requirements. To address this, this paper proposes a multilevel embedding refinement framework (MERIT) based on large-scale graphs, using spectral distance-constrained graph coarsening algorithms and an improved graph convolutional neural network model. Experimental results show the effectiveness of MERIT, with an average AUROC score 8% higher than other baseline methods.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Psychiatry
Cristiana Dimulescu, Serdar Gareayaghi, Fabian Kamp, Sophie Fromm, Klaus Obermayer, Christoph Metzner
Summary: The coordinated dynamic interactions of large-scale brain circuits and networks are associated with cognitive functions and behavior. The anatomical organization of these networks constrains the dynamical landscape of brain activity, affecting the states and transitions the brain can display. Large-scale dysconnectivity may play a crucial role in the pathophysiology of schizophrenia.
FRONTIERS IN PSYCHIATRY
(2021)
Article
Computer Science, Theory & Methods
Wissem Inoubli, Sabeur Aridhi, Haithem Mezni, Mondher Maddouri, Engelbert Mephu Nguifo
Summary: Graph clustering is a key technique for understanding structures in networks, with most algorithms currently more suitable for small graphs and lacking significant support for large-scale networks. The proposed distributed graph clustering algorithm shows higher efficiency in large networks compared to existing methods.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Article
Automation & Control Systems
Shawon Dey, Hao Xu
Summary: This paper introduces a distributed adaptive formation control for large-scale multi-agent systems that addresses the computational complexity and communication traffic challenges while extending distributed control from small scale to large scale. A novel hierarchical game theoretic algorithm is developed to provide a feasible theory foundation for solving the optimal formation problem. The effectiveness of the presented schemes is demonstrated through numerical simulations and Lyapunov analysis.
IET CONTROL THEORY AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Xiangyu Luo, Yingxiao Luo, Gang Xin, Xiaolin Gui, Jia Wang, Cheng Guo
Summary: This paper investigates the partitioning problem of dynamic and large-scale graphs, proposing a practical and high-quality graph partitioning algorithm that reduces the number of cross partition edges while maintaining a similar level of load balance by assigning vertices in the delta graph to properly chosen partitions.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Xiaofeng Ding, Cui Wang, Kim-Kwang Raymond Choo, Hai Jin
Summary: The paper introduces a new k-decomposition algorithm and an information loss matrix designed for utility measurement in massively large graph datasets. Additionally, a novel privacy preserving framework is proposed to seamlessly integrate with graph storage, anonymization, query processing, and analysis.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Engineering, Electrical & Electronic
Yujie Fu, Pengju Zhang, Bingxi Liu, Zheng Rong, Yihong Wu
Summary: Most image matching methods fail to handle large scale changes in images. To address this issue, we propose a Scale-Difference-Aware Image Matching method (SDAIM) that reduces image scale differences before local feature extraction. Our proposed Covisibility-Attention-Reinforced Matching module (CVARM) accurately estimates the scale ratio for SDAIM, and the Scale-Net improves upon existing scale ratio estimation methods. Quantitative and qualitative experiments demonstrate that Scale-Net has higher accuracy and better generalization ability. Moreover, SDAIM and Scale-Net significantly enhance the performance of local feature matching methods.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2023)
Article
Biotechnology & Applied Microbiology
Prashant Pandey, Yinjie Gao, Carl Kingsford
Summary: VariantStore efficiently indexes genomic variants from multiple samples using a variation graph, enabling variant queries across different sample-specific coordinate systems. It demonstrates scalability by indexing genomic variants from the TCGA and 1000 Genomes projects in a short amount of time, with queries for gene variants taking between 0.002 and 3 seconds, using only 10% of the full representation's memory.
Article
Computer Science, Information Systems
Xingwang Zhao, Jiye Liang, Jie Wang
Summary: This paper proposes a community detection algorithm based on graph compression, which merges vertices, defines density and quality indicators, and simultaneously determines the number of communities and initial seeds, achieving analysis of community structure of social networks. Experiments demonstrate the superiority of this method over existing community detection algorithms.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Chen Chen, Ruiyue Peng, Lei Ying, Hanghang Tong
Summary: Connectivity in networks is crucial in various high-impact applications, but the problem of connectivity minimization remains challenging, requiring a balance between optimization quality and computational efficiency.
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
(2021)
Article
Oncology
Cecilie R. Buskbjerg, Robert Zachariae, Simon Buus, Claus H. Gravholt, Lene Haldbo-Classen, S. M. Hadi Hosseini, Ali Amidi
Summary: The study assessed cognitive impairment in untreated prostate cancer patients referred to androgen-deprivation therapy (ADT) and found a higher frequency of cognitive impairment compared to healthy controls. Results showed that cognitive performance was associated with patient-reported outcome measures, brain networks structure, and testosterone levels.
Article
Multidisciplinary Sciences
Joseph M. Baker, Jennifer L. Bruno, Aaron Piccirilli, Andrew Gundran, Lene K. Harbott, David. M. Sirkin, Matthew Marzelli, S. M. Hadi Hosseini, Allan L. Reiss
Summary: Smartphone use during driving leads to changes in brain and behavioral indices of drivers, which in turn affects vehicle control, indicating negative impacts on driving safety.
SCIENTIFIC REPORTS
(2021)
Article
Psychiatry
Jennifer L. Bruno, David S. Hong, Amy A. Lightbody, S. M. Hadi Hosseini, Joachim Hallmayer, Allan L. Reiss
Summary: Individuals with Fragile X syndrome who carry the BclI polymorphism of the glucocorticoid receptor gene NR3C1 may exhibit attenuated symptoms of anxiety/depression and externalizing behaviors, with structural neuroimaging data able to differentiate between genotypes. Key regions of anxiety/fear neurocircuitry play a significant role in distinguishing groups.
JOURNAL OF PSYCHIATRIC RESEARCH
(2021)
Article
Neurosciences
Elveda Gozdas, Hannah Fingerhut, Hua Wu, Jennifer L. Bruno, Lauren Dacorro, Booil Jo, Ruth O'Hara, Allan L. Reiss, S. M. Hadi Hosseini
Summary: Healthy and pathological aging affect brain microstructure through complex processes. The study demonstrated that reduced MTV in association tracts is linked to older age in healthy aging, correlated with memory performance, and distinguishes aMCI from controls. Additionally, changes in gray matter tissue properties were also documented, showing a widespread decrease in R1 with age and decreased R1 in aMCI compared to controls.
Article
Neuroimaging
Cecilie R. Buskbjerg, Robert Zachariae, Mads Agerbaek, Claus H. Gravholt, Lene Haldbo-Classen, S. M. Hadi Hosseini, Ali Amidi
Summary: Compared to healthy controls, newly orchiectomized testicular cancer patients performed worse on 6 out of 15 neuropsychological tests, with 3 tests remaining statistically significant after adjusting for relevant between-group differences. Testicular cancer patients also demonstrated a higher incidence of cognitive impairment (65% vs. 36%) and showed regional differences in node degree and betweenness centrality in brain network analysis. In testicular cancer patients, CAG repeat length was positively correlated with delayed memory performance, and a COMT genotype interaction effect was found for overall cognitive performance.
BRAIN IMAGING AND BEHAVIOR
(2022)
Article
Neurosciences
Elveda Gozdas, Hannah Fingerhut, Lauren Dacorro, Jennifer L. Bruno, S. M. Hadi Hosseini
Summary: Aging is associated with reduced neurite density and orientation dispersion in both cortical and white matter regions. Additionally, a widespread age-related decrease in neurite density along major white matter tracts was found in healthy older adults. Significant neurite morphology deficits in memory networks were also observed in patients with amnestic mild cognitive impairment.
Review
Clinical Neurology
Louisa K. Gosse, Sarah W. Bell, S. M. Hadi Hosseini
Summary: Research has shown a link between EF deficits and behavioral symptoms of ADHD. Neuroimaging studies support the involvement of EF impairment in ADHD. While fNIRS is increasingly used in ADHD research, its reliability compared to fMRI studies has not been qualitatively evaluated. Qualitative analysis of fNIRS studies shows consistent hypoactivity in the right prefrontal cortex in ADHD, which is corroborated by altered activity in fMRI studies.
EUROPEAN ARCHIVES OF PSYCHIATRY AND CLINICAL NEUROSCIENCE
(2022)
Article
Oncology
Cecilie R. Buskbjerg, Ali Amidi, Simon Buus, Claus H. Gravholt, S. M. Hadi Hosseini, Robert Zachariae
Summary: The study suggests that prostate cancer patients undergoing ADT may experience cognitive decline, with COMT Met homozygotes potentially at higher risk. However, changes in brain connectomes and testosterone levels were not shown to be underlying mechanisms for cognitive decline. Further research is needed to evaluate the impact of ADT on the hypothalamic-pituitary-gonadal axis dynamics.
PROSTATE CANCER AND PROSTATIC DISEASES
(2022)
Article
Oncology
Cecilie R. Buskbjerg, Ali Amidi, Mads Agerbaek, Claus H. Gravholt, Sm Hadi Hosseini, Robert Zachariae
Summary: The study confirms specific cognitive decline in TCPs following orchiectomy, but also indicates specific cognitive improvements. The results do not suggest changes in brain connectomes or endocrine status as the main drivers of cognitive decline.
Article
Neurosciences
Barbara Avelar-Pereira, Grace K. -Y. Tam, S. M. Hadi Hosseini
Summary: This study investigates the effect of body posture on connectivity in healthy adults by using resting-state fMRI. The results indicate that connectivity remains unchanged in the supine and left lateral decubitus positions, but is altered in the right lateral decubitus position. Combining fMRI with other techniques can provide a better understanding of the phenomenon of interest.
BRAIN CONNECTIVITY
(2022)
Article
Neurosciences
Hannah Fingerhut, Elveda Gozdas, S. M. Hadi Hosseini
Summary: This study investigated the impact of education and occupation on white matter tracts in patients with amnestic mild cognitive impairment (aMCI) and healthy elders. The results showed that higher levels of education and occupation were associated with worse white matter pathology in aMCI patients, but with better white matter properties in healthy elders.
JOURNAL OF ALZHEIMERS DISEASE
(2022)
Article
Biochemistry & Molecular Biology
Barbara Avelar-Pereira, Michael E. Belloy, Ruth O'Hara, S. M. Hadi Hosseini
Summary: This study utilizes a multi-dimensional network framework for the detection and analysis of Alzheimer's disease (AD). The results show that multilayer community detection accurately identifies AD and can identify clinically misdiagnosed cases. Additionally, this method can identify subtypes with similar multidisciplinary profiles, providing unique insight into the heterogeneity of AD.
MOLECULAR PSYCHIATRY
(2023)
Article
Neurosciences
Rihui Li, Hadi Hosseini, Manish Saggar, Stephanie Christina Balters, Allan L. Reiss
Summary: Functional near-infrared spectroscopy (fNIRS) is a noninvasive optical imaging technique used to assess human brain activity. It has been widely used in psychiatric research and clinical practice due to its mobility, low cost, and tolerance for motion. This article summarizes the applications of fNIRS in psychiatry, the development of fNIRS instruments, and new study designs to explore brain activity associated with psychiatric disorders. The challenges and future perspectives of fNIRS in psychiatric research and clinical practice are also discussed.
Article
Neuroimaging
Elveda Gozdas, Lauren Hinkley, Hannah Fingerhut, Lauren Dacorro, Meng Gu, Matthew Sacchet, Ralph Hurd, S. M. Hadi Hosseini
Summary: This study investigated alterations in neurometabolites in the prefrontal cortex and their relationship with cortical microstructure in Alzheimer's disease (AD). The results showed significant changes in neurometabolite levels in patients with amnestic Mild Cognitive Impairments (aMCI) compared to healthy controls. Additionally, there were correlations between neuronal intracellular compartment and neurite density, as well as between neurite orientation and myo-inositol levels in healthy older adults only. These findings provide unique evidence regarding the imbalance in the association between neurometabolites and neurite microstructure in the early stage of AD.
NEUROIMAGE-CLINICAL
(2022)
Article
Engineering, Electrical & Electronic
Francis Tsow, Anupam Kumar, S. M. Hadi Hosseini, Audrey Bowden
Summary: Advancements in technology have increased interest in using functional near-infrared spectroscopy (fNIRS) for neuromonitoring in naturalistic environments. This article introduces an open source, fully integrated wireless fNIRS headband system with a user-friendly and comfortable design for easy data collection in research.
