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Jinhuan Wang

Datos Biográficos

ID3928093
NOMBREJinhuan Wang
NOMBRESJinhuan
APELLIDOWang
FIRMAWANG J
AFILIACIONESChongqing University
ORCID0000-0001-9348-3424
VERIFICADOSí
TOTAL DE OBRAS14
TOTAL DE CITAS35
OBRAS COMO AUTOR14
OBRAS COMO EDITOR0
PRIMER AÑO DE PUBLICACIÓN2006
AÑO MÁS RECIENTE DE PUBLICACIÓN2025
ÍNDICE H4
  • Hierarchical machine learning model integrating clinical history and nursing observations for predicting violent behavior in hospitalized schizophrenia patients

    Open Access•Xianfeng Meng, Liang Wang et al.•ARTICLE•Frontiers in Psychiatry•2025

    The hierarchical machine learning model integrating clinical history and dynamic nursing observations significantly enhances predictive accuracy for violent behavior in schizophrenia inpatients, providing clinicians with valuable tools for timely risk assessment and personalized preventive interventions

  • Associations among mental well-being, marital quality, and maternal depressive symptoms in China

    Open Access•Jinhuan Wang, Wang Jinhuan et al.•ARTICLE•Frontiers in Psychiatry•2025

    PPD is a significant postpartum issue that is influenced by numerous factors. Early screening of parturients, accurate diagnosis of PPD, and timely intervention are crucial. Targeted interventions addressing risk factors may help to mitigate the incidence of PPD

  • DeepInsight

    Open Access•Junhao Zhu, Jinhuan Wang et al.•ARTICLE•IEEE Transactions on Computational…•2024

    With the rapid development of artificial intelligence, a number of machine learning algorithms, such as graph neural networks (GNNs), have been proposed to facilitate network analysis or graph data mining. Although effective, recent studies show that these advanced methods may suffer from adversarial attacks, i.e., they may lose effectiveness when only a small fraction of links are unexpectedly changed. This article investigates three well-known …

  • Node Injection Attack Based on Label Propagation Against Graph Neural Network

    Open Access•Peican Zhu, Zechen Pan et al.•ARTICLE•IEEE Transactions on Computational…•2024

    Graph neural network (GNN) has achieved remarkable success in various graph learning tasks, such as node classification, link prediction, and graph classification. The key to the success of GNN lies in its effective structure information representation through neighboring aggregation. However, the attacker can easily perturb the aggregation process through injecting fake nodes, which reveals that GNN is vulnerable to the graph injection attack (G…

  • GA-Based Multipopulation Synergistic Gene Screening Strategy on Critical Nodes Detection

    Open Access•Shanqing Yu, Jiaxiang Li et al.•ARTICLE•IEEE Transactions on Computational…•2024

    Critical node detection (CND) is commonly used to detect nodes with a high impact on network robustness. It has been widely used in disease propagation, social networks, communications, and other fields. As a nondeterministic polynomial-time (NP)-complete problem, the efficiency of solving CND severely limits the scale of the available network. Fortunately, the evolutionary algorithm (EA) is an effective method to solve this problem. However, alt…

  • Single-Node Injection Label Specificity Attack on Graph Neural Networks via Reinforcement Learning

    Open Access•Dayuan Chen, Jian Zhang et al.•ARTICLE•IEEE Transactions on Computational…•2024

    Graph neural networks (GNNs) have achieved remarkable success in various real-world applications. However, recent studies highlight the vulnerability of GNNs to malicious perturbations. Previous adversaries primarily focus on graph modifications or node injections to existing graphs, yielding promising results but with notable limitations. Graph modification attack (GMA) requires manipulation of the original graph, which is often impractical, whi…

  • Attacking the Core Structure of Complex Network

    Open Access•Bo Zhou, Yuqian Lv et al.•ARTICLE•IEEE Transactions on Computational…•2023

    The concept of $k$ -core in complex networks plays a key role in many applications, e.g., understanding the global structure or identifying central/critical nodes, of a network. A malicious attacker with a jamming ability can exploit the vulnerability of the $k$ -core structure to attack the network and invalidate the network analysis methods, e.g., reducing the $k$ -shell values of nodes can deceive graph algorithms, leading to the wrong decisio…

  • Can urban agglomeration bring “1 + 1 > 2Effect”? A perspective of land resource carrying capacity

    Open Access•Liyin Shen, Guangyu Cheng et al.•ARTICLE•Land Use Policy•2022•Citada por: 9•Referencias: 104

  • How to address properly the scale of urban infrastructures? –An empirical study of 35 large Chinese cities

    Open Access•Yitian Ren, Liyin Shen et al.•ARTICLE•Habitat International•2021•Citada por: 3•Referencias: 72

  • An integrative method for analyzing spatial accessibility in the hierarchical diagnosis and treatment system in China

    Open Access•Nan Yang, Liyin Shen et al.•ARTICLE•Social Science & Medicine•2021•Citada por: 3•Referencias: 30

  • A novel evaluation method for urban infrastructures carrying capacity

    Open Access•Jinhuan Wang, Yitian Ren et al.•ARTICLE•Cities•2020•Citada por: 6•Referencias: 67

  • Economic perspective-based analysis on urban infrastructures carrying capacity — A China study

    Open Access•Jinhuan Wang, Yitian Ren et al.•ARTICLE•Environmental Impact Assessment…•2020•Citada por: 6•Referencias: 51

  • An alternative model for evaluating the balance of carrying capacity between functional urban infrastructures

    Open Access•Jinhuan Wang, Liyin Shen et al.•ARTICLE•Environmental Impact Assessment…•2019•Citada por: 8•Referencias: 69

  • Chromosome painting between human and lorisiform prosimians

    Open Access•Wenhui Nie, Patricia C M O'Brien et al.•ARTICLE•American Journal of Physical…•2006•Referencias: 45

    Multidirectional chromosome painting with probes derived from flow‐sorted chromosomes of humans ( Homo sapiens , HSA, 2n = 46) and galagos ( Galago moholi , GMO, 2n = 38) allowed us to map evolutionarily conserved chromosomal segments among humans, galagos, and slow lorises ( Nycticebus coucang , NCO, 2n = 50). In total, the 22 human autosomal painting probes detected 40 homologous chromosomal segments in the slow loris genome. The genome of the …

  • Can urban agglomeration bring “1 + 1 > 2Effect”? A perspective of land resource carrying capacity

    Open Access•Liyin Shen, Guangyu Cheng et al.•ARTICLE•Land Use Policy•2022•Citada por: 9•Referencias: 104

  • An alternative model for evaluating the balance of carrying capacity between functional urban infrastructures

    Open Access•Jinhuan Wang, Liyin Shen et al.•ARTICLE•Environmental Impact Assessment…•2019•Citada por: 8•Referencias: 69

  • A novel evaluation method for urban infrastructures carrying capacity

    Open Access•Jinhuan Wang, Yitian Ren et al.•ARTICLE•Cities•2020•Citada por: 6•Referencias: 67

  • Economic perspective-based analysis on urban infrastructures carrying capacity — A China study

    Open Access•Jinhuan Wang, Yitian Ren et al.•ARTICLE•Environmental Impact Assessment…•2020•Citada por: 6•Referencias: 51

  • How to address properly the scale of urban infrastructures? –An empirical study of 35 large Chinese cities

    Open Access•Yitian Ren, Liyin Shen et al.•ARTICLE•Habitat International•2021•Citada por: 3•Referencias: 72

  • An integrative method for analyzing spatial accessibility in the hierarchical diagnosis and treatment system in China

    Open Access•Nan Yang, Liyin Shen et al.•ARTICLE•Social Science & Medicine•2021•Citada por: 3•Referencias: 30

  • Chromosome painting between human and lorisiform prosimians

    Open Access•Wenhui Nie, Patricia C M O'Brien et al.•ARTICLE•American Journal of Physical…•2006•Referencias: 45

    Multidirectional chromosome painting with probes derived from flow‐sorted chromosomes of humans ( Homo sapiens , HSA, 2n = 46) and galagos ( Galago moholi , GMO, 2n = 38) allowed us to map evolutionarily conserved chromosomal segments among humans, galagos, and slow lorises ( Nycticebus coucang , NCO, 2n = 50). In total, the 22 human autosomal painting probes detected 40 homologous chromosomal segments in the slow loris genome. The genome of the …

  • An alternative model for evaluating the balance of carrying capacity between functional urban infrastructures

    Open Access•Jinhuan Wang, Liyin Shen et al.•ARTICLE•Environmental Impact Assessment…•2019•Citada por: 8•Referencias: 69

  • A novel evaluation method for urban infrastructures carrying capacity

    Open Access•Jinhuan Wang, Yitian Ren et al.•ARTICLE•Cities•2020•Citada por: 6•Referencias: 67

  • Economic perspective-based analysis on urban infrastructures carrying capacity — A China study

    Open Access•Jinhuan Wang, Yitian Ren et al.•ARTICLE•Environmental Impact Assessment…•2020•Citada por: 6•Referencias: 51

  • How to address properly the scale of urban infrastructures? –An empirical study of 35 large Chinese cities

    Open Access•Yitian Ren, Liyin Shen et al.•ARTICLE•Habitat International•2021•Citada por: 3•Referencias: 72

  • An integrative method for analyzing spatial accessibility in the hierarchical diagnosis and treatment system in China

    Open Access•Nan Yang, Liyin Shen et al.•ARTICLE•Social Science & Medicine•2021•Citada por: 3•Referencias: 30

  • Can urban agglomeration bring “1 + 1 > 2Effect”? A perspective of land resource carrying capacity

    Open Access•Liyin Shen, Guangyu Cheng et al.•ARTICLE•Land Use Policy•2022•Citada por: 9•Referencias: 104

  • Attacking the Core Structure of Complex Network

    Open Access•Bo Zhou, Yuqian Lv et al.•ARTICLE•IEEE Transactions on Computational…•2023

    The concept of $k$ -core in complex networks plays a key role in many applications, e.g., understanding the global structure or identifying central/critical nodes, of a network. A malicious attacker with a jamming ability can exploit the vulnerability of the $k$ -core structure to attack the network and invalidate the network analysis methods, e.g., reducing the $k$ -shell values of nodes can deceive graph algorithms, leading to the wrong decisio…

  • DeepInsight

    Open Access•Junhao Zhu, Jinhuan Wang et al.•ARTICLE•IEEE Transactions on Computational…•2024

    With the rapid development of artificial intelligence, a number of machine learning algorithms, such as graph neural networks (GNNs), have been proposed to facilitate network analysis or graph data mining. Although effective, recent studies show that these advanced methods may suffer from adversarial attacks, i.e., they may lose effectiveness when only a small fraction of links are unexpectedly changed. This article investigates three well-known …

  • Node Injection Attack Based on Label Propagation Against Graph Neural Network

    Open Access•Peican Zhu, Zechen Pan et al.•ARTICLE•IEEE Transactions on Computational…•2024

    Graph neural network (GNN) has achieved remarkable success in various graph learning tasks, such as node classification, link prediction, and graph classification. The key to the success of GNN lies in its effective structure information representation through neighboring aggregation. However, the attacker can easily perturb the aggregation process through injecting fake nodes, which reveals that GNN is vulnerable to the graph injection attack (G…

  • GA-Based Multipopulation Synergistic Gene Screening Strategy on Critical Nodes Detection

    Open Access•Shanqing Yu, Jiaxiang Li et al.•ARTICLE•IEEE Transactions on Computational…•2024

    Critical node detection (CND) is commonly used to detect nodes with a high impact on network robustness. It has been widely used in disease propagation, social networks, communications, and other fields. As a nondeterministic polynomial-time (NP)-complete problem, the efficiency of solving CND severely limits the scale of the available network. Fortunately, the evolutionary algorithm (EA) is an effective method to solve this problem. However, alt…

  • Single-Node Injection Label Specificity Attack on Graph Neural Networks via Reinforcement Learning

    Open Access•Dayuan Chen, Jian Zhang et al.•ARTICLE•IEEE Transactions on Computational…•2024

    Graph neural networks (GNNs) have achieved remarkable success in various real-world applications. However, recent studies highlight the vulnerability of GNNs to malicious perturbations. Previous adversaries primarily focus on graph modifications or node injections to existing graphs, yielding promising results but with notable limitations. Graph modification attack (GMA) requires manipulation of the original graph, which is often impractical, whi…

  • Hierarchical machine learning model integrating clinical history and nursing observations for predicting violent behavior in hospitalized schizophrenia patients

    Open Access•Xianfeng Meng, Liang Wang et al.•ARTICLE•Frontiers in Psychiatry•2025

    The hierarchical machine learning model integrating clinical history and dynamic nursing observations significantly enhances predictive accuracy for violent behavior in schizophrenia inpatients, providing clinicians with valuable tools for timely risk assessment and personalized preventive interventions

  • Associations among mental well-being, marital quality, and maternal depressive symptoms in China

    Open Access•Jinhuan Wang, Wang Jinhuan et al.•ARTICLE•Frontiers in Psychiatry•2025

    PPD is a significant postpartum issue that is influenced by numerous factors. Early screening of parturients, accurate diagnosis of PPD, and timely intervention are crucial. Targeted interventions addressing risk factors may help to mitigate the incidence of PPD

Computer Science (9 obras) · Engineering (6 obras) · Urban Transport and Accessibility (6 obras) · Business (5 obras) · Economics (5 obras) · Environmental planning (5 obras) · Geography (5 obras) · Water Resources and Sustainability (5 obras) · Advanced Graph Neural Networks (4 obras) · Carrying capacity (4 obras)

Ethnos_APP • Proyecto Open Source • Licencia MIT • Frontend v2.0.0 • Privacidad y Cookies • Documentación de la API: api.ethnos.app/docs • Código de la API: GitHub • DOI: 10.5281/zenodo.17049435 • Código del Frontend: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae