Core Nodes Research Articles

Distinct Classes of Statistical Anxiety: Latent Profile and Network Psychometrics Analysis of University Students.

Many university students will experience statistical anxiety. Consequentially, the relationship between such anxiety and learning performance has been of concern to various educational researchers. To date, however, there has been no consistent resolution to this problem. Because previous studies have mainly used the perspective of variant-centered analysis rather than taking into account individual differences, this study argues that the different classes of statistical anxiety among university students may be an important influencing factor. In this study, 1607 Chinese university students who had just completed a statistics course were assessed using the Statistical Anxiety Scale, Statistics Learning Self-Efficacy Scale, and Learning Engagement Scale, and an exploratory study was conducted to determine whether university students' statistical anxiety could be divided into different classes. Latent profile and network psychometrics analyses were then used to analyze the data. (1) The latent profile analysis found that university students' statistical anxiety could be divided into three different latent classes: mild test anxiety, moderate text anxiety, and severe statistical anxiety. (2) The correlation analysis showed that the relationships among the three latent classes of statistical anxiety and learning performance were not entirely consistent, indicating that there was heterogeneity in the statistical anxiety of these university students. (3) Further network psychometrics analysis showed that the statistical anxiety network structure of the three latent classes has different core nodes that reflected the most important symptoms of statistical anxiety. There is heterogeneity in university students' statistical anxiety that can be divided into three latent classes. These core nodes in the statistical anxiety networks of the three latent classes were different, helping statistics instructors to better understand the nature of these latent classes, take different intervention measures for different latent classes of university students.

Optimizing Genomic Control in Hit Network-Target Set Model Associations with Lung Adenocarcinoma.

Background: Hit network-target sets (HNSs), compiled sets of different network nodes of the same type, are available and play a significant role in cancer development but are notoriously more difficult to select than a single target. This is due to a combination of challenges attributed to the differential of node interactions, node heterogeneity, and the limitations of node-hit information. Methods: In this study, we constructed a lung adenocarcinoma regulatory network using TCGA data and obtained different HNSs of driver nodes (DNs), core modules (CMs) and core nodes (CNs) through three kinds of methods. Then, the optimized HNS (OHNS) was obtained by integrating CMs, CNs and DNs, and the performance of different HNSs was evaluated according to network structure importance, control capability, and clinical value. Results: We found that the OHNS has two main advantages, the central location of the network and the ability to control the network, and it plays an important role in the disease network through its multifaceted capabilities. Three unique pathways were discovered in the OHNS, which is consistent with previous experiments. Additionally, 13 genes were predicted to play roles in risk prognosis, disease drivers, and cell perturbation effects of lung adenocarcinoma, of which 12 may be candidates for new drugs and biomarkers of lung adenocarcinoma. Conclusion: This study can help us understand and control a network more effectively to determine the development trend of a disease, design effective multitarget drugs, and guide the therapeutic community to optimize appropriate strategies according to different research aims in cancer treatment.

Sensor Network Structure Recognition Based on P-law

A sensor graph network is a sensor network model organized according to graph network structure. Structural unit and signal propagation of core nodes are the basic characteristics of sensor graph networks. In sensor networks, network structure recognition is the basis for accurate identification and effective prediction and control of node states. Aiming at the problems of difficult global structure identification and poor interpretability in complex sensor graph networks, based on the characteristics of sensor networks, a method is proposed to firstly unitize the graph network structure and then expand the unit based on the signal transmission path of the core node. This method which builds on unit patulousness and core node signal propagation (called p-law) can rapidly and effectively achieve the global structure identification of a sensor graph network. Different from the traditional graph network structure recognition algorithms such as modularity maximization and spectral clustering, the proposed method reveals the natural evolution process and law of graph network subgroup generation. Experimental results confirm the effectiveness, accuracy and rationality of the proposed method and suggest that our method can be a new approach for graph network global structure recognition.

基于MSPA模型的新型城市热景观连通性分析

PDF HTML阅读 XML下载 导出引用 引用提醒 基于MSPA模型的新型城市热景观连通性分析 DOI: 10.5846/stxb202109172619 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 湖南省科技创新计划湖湘青年英才项目（2021RC3104）；湖南省研究生科研创新基金项目（CX20210895）；株洲云龙示范区生态廊道专项规划项目（HNHXZZ-20171109） Analysis connectivity of urban heat island in a new-type urbanization based on MSPA model Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:基于土地利用景观格局对城市热岛效应的影响研究较多，但准确识别对城市热岛效应具有重要影响的关键斑块和节点方面的研究亟待开展。以新型中小城市株洲云龙示范区为例，以城市热岛网络构建为切入点，应用形态学空间格局分析（Morphological Spatial Pattern Analysis，MSPA）与景观连通性模型识别城市发展进程中核心热景观斑块和关键节点，并分析其时空演变特征。研究结果表明：（1）2007-2018年云龙示范区热岛斑块趋于集中，热岛面积较研究初期有明显增加，从地表温度空间分布上体现为城区与周边农村的温度对比更加鲜明，表明城市化进程中建设用地呈聚集扩散趋势。（2）研究期间热岛核心区占热景观面积比最大，且比例有明显上升趋势。而边缘、支线、孤岛、桥接、环线以及孔隙形态类型面积占比均呈现出不同程度的下降趋势，表明热岛斑块之间聚集程度不断提高。（3）识别出景观连通度重要性值较高的前20热景观斑块，其中前3斑块的重要性值和面积占比较大，属于极重要与重要两个热岛核心斑块重要性等级，值得重点关注与优化。（4）对重要性值高的热岛核心区域可优先采取降温措施，可以打断节点之间连接减缓整个区域的热岛效应。本研究结果对我国中小城市规划和可持续发展具有重要的实践指导意义。 Abstract:Many previous studies have concerned with the effects of land use/land cover changes and landscape patterns on the urban heat island. However, accurate identification of key patches and nodes with high contribution to urban heat island need to be urgently studied. Using Yunlong Demonstration Zone of Zhuzhou, a new-type city, as the case study area based on morphological spatial pattern analysis (MSPA) and landscape connectivity model, this study constructed an urban heat island network to identify the spatio-temporal dynamic characteristics of core thermal landscape patches and nodes in process of urban development. The results showed that:(1) from 2007 to 2018, heat island patches tended to be concentrated in Yunlong Demonstration Zone, and the area of heat island increased significantly compared with the initial stage of the study. From the spatial distribution of land surface temperature, the contrast between urban area and surrounding countryside was more distinct. (2) During the study period, the area of heat island core class was the largest, and the area proportion showed an obviously rising trend. While the area proportion of edge class, branch class, islet class, bridge class, loop class and perforation class showed a decreasing trend to varying degrees, which was largely related to the increase of aggregation degree between heat island patches. (3) The top 20 thermal landscape patches with higher importance value of landscape connectivity were identified, and the importance value and area of the top 3 patches were relatively large, which should be deserved more attention and optimization. (4) Patch-based cooling measures adopted to avoid the connection between node could effectively alleviate the heat island effects in the whole region. The results of this study have important practical significance for the planning and sustainable development of small and medium-sized cities in China. 参考文献 相似文献 引证文献

Sex Differences in the Associations Between Transdiagnostic Trauma Distress and Alcohol Cue-Reactivity in AUD: Implications for TMS Target Selection

Abstract Introduction: TMS has shown great promise for the treatment of depression, OCD, and other psychiatric conditions, with some evidence for sex differences in efficacy. However, the precise targeting of TMS for alcohol use disorder (AUD) and associated traumatic experiences remains unclear. According to neurobiological literature, brain function during alcohol cue-reactivity may play a key role in TMS target selection, however activation patterns can also vary by sex. Objective: Taken together, we aimed to 1) examine the relationship between a transdiagnostic trauma metric and alcohol cue-reactivity in males and females separately, and 2) compare the transdiagnostic trauma approach to traditional PTSD diagnosis. Methods: In 72 Veterans (16F) with AUD, trauma distress transdiagnostic factor was previously identified through principal components analysis of self-reported symptoms. Bivariate correlations assessed how trauma distress relates to activation and functional connectivity in a-priori defined, core incentive salience nodes during an fMRI cue-reactivity task (CUE) in females and males separately. Independent samples t-tests compared the same metrics by PTSD diagnosis. Results: In females, transdiagnostic trauma distress was significantly associated with hypoactivation in the left lateral PFC and decreased connectivity in incentive salience regions (L.NAcc to L.Amygdala and R.NAc to L.Amygdala, L.AntInsula, L.NAcc, L.posInsula, R.Putamen; -0.6<r<-0.5, p<0.05). By contrast, in males, trauma distress was not associated with CUE activation, nor functional connectivity. Further, neither females nor males showed differences in CUE activation or connectivity based on PTSD diagnosis Conclusions: Females with higher trauma distress showed decreased activation and connectivity in core nodes of the incentive salience network during an alcohol cue-reactivity task, while males did not. Therefore, separate analyses by sex may be beneficial to define neuromodulation targets for those with AUD and traumatic experiences. Further, examining trauma symptoms continuously and transdiagnostically may better explain brain function in AUD than binary PTSD diagnosis. Research Category and Technology and Methods Clinical Research: 18. Functional Brain Imaging Keywords: Alcohol Use Disorder, Trauma, Cue-Reactivity, Sex Differences

Performance Comparison of JIT and JET Protocols on the OBS Networks

Over the past few years, internet traffic has been explosive growth due to users' need for more applications and services. All these developments on the internet need more bandwidth. The Optical Burst Switching (OBS) mechanism along with Wavelength-Division-Multiplexing (WDM) technology is very useful in meeting the high bandwidth requirements. However, the OBS network uses a one-way resource reservation mechanism, as well as it is a buffer lack at the intermediate (core) node. Therefore, the OBS network depends on contention resolution mechanisms to achieve acceptable levels of performance. In this paper, the Deflection Routing (DR) mechanism is used to overcome contention. The performance of the OBS network has been estimated by executing Just-In-Time (JIT) and Just-Enough-Time (JET) signaling reservation protocols with the DR. NCTUns-6.0 simulator was employed to verify the performance of the OBS network based on two metrics: throughput and packets dropped in the network. The results show that the bandwidth resources utilization in the OBS network with JET and DR (OBS-JET-DR) is better than in the OBS network with JIT and DR (OBS-JIT-DR). However, the average number of packets dropped in OBS-JET-DR is less than OBS-JIT-DR by 21%. In addition, the throughput of OBS-JET-DR has increased by 4% compared with OBS-JIT-DR.

빅데이터 분석을 활용한 비건음식 소비자 인식연구

This study analyzed consumers' perceptions of 'Vegan food', which has recently become a global issue, with big data. Text data from 2020 to 2022 were collected from Daum and Naver with the word ‘Vegan food’ in using Textom. The results of this study are intended to provide relevant data to vegan food and food researchers. A total of 3,866(1,469KB) cases of collected data were analyzed for the data refinement process based on the collected data. Afterwards, frequency analysis and TF-IDF analysis were performed to extract the top 50 key words with high frequency of exposure. The density index of the network was identified through the degree of connectivity centrality analysis, and the connectivity relationship between component nodes was visualized based on centrality. and then the CONCOR analysis was performed through the connectivity relationship of core nodes to classify into four clusters.

Identify Biomarkers and Design Effective Multi-Target Drugs in Ovarian Cancer: Hit Network-Target Sets Model Optimizing.

Epithelial ovarian cancer (EOC) is highly aggressive with poor patient outcomes, and a deeper understanding of ovarian cancer tumorigenesis could help guide future treatment development. We proposed an optimized hit network-target sets model to systematically characterize the underlying pathological mechanisms and intra-tumoral heterogeneity in human ovarian cancer. Using TCGA data, we constructed an epithelial ovarian cancer regulatory network in this study. We use three distinct methods to produce different HNSs for identification of the driver genes/nodes, core modules, and core genes/nodes. Following the creation of the optimized HNS (OHNS) by the integration of DN (driver nodes), CM (core module), and CN (core nodes), the effectiveness of various HNSs was assessed based on the significance of the network topology, control potential, and clinical value. Immunohistochemical (IHC), qRT-PCR, and Western blotting were adopted to measure the expression of hub genes and proteins involved in epithelial ovarian cancer (EOC). We discovered that the OHNS has two key advantages: the network's central location and controllability. It also plays a significant role in the illness network due to its wide range of capabilities. The OHNS and clinical samples revealed the endometrial cancer signaling, and the PI3K/AKT, NER, and BMP pathways. MUC16, FOXA1, FBXL2, ARID1A, COX15, COX17, SCO1, SCO2, NDUFA4L2, NDUFA, and PTEN hub genes were predicted and may serve as potential candidates for new treatments and biomarkers for EOC. This research can aid in better capturing the disease progression, the creation of potent multi-target medications, and the direction of the therapeutic community in the optimization of effective treatment regimens by various research objectives in cancer treatment.

EEG source derived salience network coupling supports real-world attention switching

While the brain mechanisms underlying selective attention have been studied in great detail in controlled laboratory settings, it is less clear how these processes function in the context of a real-world self-paced task. Here, we investigated engagement on a real-world computerized task equivalent to a standard academic test that consisted of solving high-school level problems in a self-paced manner. In this task, we used EEG-source derived estimates of effective coupling between brain sources to characterize the neural mechanisms underlying switches of sustained attention from the attentive on-task state to the distracted off-task state. Specifically, since the salience network has been implicated in sustained attention and attention switching, we conducted a hypothesis-driven analysis of effective coupling between the core nodes of the salience network, the anterior insula (AI) and the anterior cingulate cortex (ACC). As per our hypothesis, we found an increase in AI - > ACC effective coupling that occurs during the transitions of attention from on-task focused to off-task distracted state. This research may inform the development of future neural function-targeted brain-computer interfaces to enhance sustained attention.

Genome-Wide Identification and Expression Analysis of the PME and PMEI Gene Families in Diospyros kaki: A Bioinformatics Study

Pectins are major components of cell walls in plants. Pectin methylesterases (PMEs) and pectin methylesterase inhibitors (PMEIs) play crucial roles in pectin synthesis and metabolism. Overall, 28 putative DkPMEs and 29 putative DkPMEIs were identified from the D. kaki genome. According to phylogenetic analysis, DkPME/DkPMEI proteins can be classified into four and five clades, respectively. Motif and gene structure analysis showed that DkPME/DkPMEI are highly conserved in the same clades, which indicates that the function of these DkPME/DkPMEI were similar. Besides, DkPME/DkPMEI genes were distributed unevenly on their corresponding chromosomes. Synteny analysis showed that PME or PMEI gene usually matched with more than one DkPME/DkPMEI in D. oleifera, D. lotus, and A. thaliana, implying that the function of these genes in D. kaki may be diverse. Expression analysis showed that DkPME/DkPMEI from the same clade exhibited diverse expression patterns, indicating that these genes might have diverse functions. Functional protein–protein interaction network analysis showed that DkPMEI21 and DkPMEI15 were core nodes and were, respectively, positive and negative regulators for carbohydrate metabolism, stress responses, and sugar signaling. This study provides a theoretical basis for the functional characteristics, evolutionary relationship, and role of these gene families in developing persimmon fruit.

The Angiotensin Antagonist Losartan Modulates Social Reward Motivation and Punishment Sensitivity via Modulating Midbrain-Striato-Frontal Circuits.

Social deficits and dysregulations in dopaminergic midbrain-striato-frontal circuits represent transdiagnostic symptoms across psychiatric disorders. Animal models suggest that interactions between the dopamine (DA) and renin-angiotensin system (RAS) may modulate learning and reward-related processes. The present study therefore examined the behavioral and neural effects of the Angiotensin II type 1 receptor (AT1R) antagonist losartan on social reward and punishment processing in humans. A preregistered randomized double-blind placebo-controlled between-subject pharmacological design was combined with a social incentive delay (SID) functional MRI (fMRI) paradigm during which subjects could avoid social punishment or gain social reward. Healthy volunteers received a single-dose of losartan (50 mg, n = 43, female = 17) or placebo (n = 44, female = 20). We evaluated reaction times (RTs) and emotional ratings as behavioral and activation and functional connectivity as neural outcomes. Relative to placebo, losartan modulated the reaction time and arousal differences between social punishment and social reward. On the neural level the losartan-enhanced motivational salience of social rewards was accompanied by stronger ventral striatum-prefrontal connectivity during reward anticipation. Losartan increased the reward-neutral difference in the ventral tegmental area (VTA) and attenuated VTA associated connectivity with the bilateral insula in response to punishment during the outcome phase. Thus, losartan modulated approach-avoidance motivation and emotional salience during social punishment versus social reward via modulating distinct core nodes of the midbrain-striato-frontal circuits. The findings document a modulatory role of the renin-angiotensin system in these circuits and associated social processes, suggesting a promising treatment target to alleviate social dysregulations.SIGNIFICANCE STATEMENT Social deficits and anhedonia characterize several mental disorders and have been linked to the midbrain-striato-frontal circuits of the brain. Based on initial findings from animal models we here combine the pharmacological blockade of the Angiotensin II type 1 receptor (AT1R) via losartan with functional MRI (fMRI) to demonstrate that AT1R blockade enhances the motivational salience of social rewards and attenuates the negative impact of social punishment via modulating the communication in the midbrain-striato-frontal circuits in humans. The findings demonstrate for the first time an important role of the AT1R in social reward processing in humans and render the AT1R as promising novel treatment target for social and motivational deficits in mental disorders.

Core node knowledge based multi-objective particle swarm optimization for dynamic community detection

Community detection aims to classify nodes with similar attributes, and it becomes more difficult when the network structure changes over time. In order to tap into the evolution of dynamic networks, nodes with high criticality in dynamic networks clearly need to be attended. In this paper, a novel dynamic community detection algorithm based on particle swarm optimization is proposed. The core nodes in the network are obtained by calculating the resistance distance of each node. The nodes associated with these core nodes that make up the constant community. The constant community should be retained is the knowledge gained from the evolution of the core nodes in two consecutive time steps. To ensure the quality of community division at the current time step and the smoothness of two consecutive time steps, a new particle updating parameter is proposed, which is based on the optimal individual at the previous time step and the population at the current time step. Compared with the other well-known related algorithms, experimental result on many real-world and synthetic networks have illustrated the higher accuracy and stability of the proposed.

To Explore the Inhibitory Mechanism of Quercetin in Thyroid Papillary Carcinoma through Network Pharmacology and Experiments.

Quercetin, a flavonoid with anti-inflammatory and anticancer properties, is expected to be an innovative anticancer therapeutic agent for papillary thyroid carcinoma (PTC). However, the downstream signaling pathways that mediate quercetin-dependent anticancer properties remain to be deciphered. Herein, potential targets of quercetin were screened with several bioinformatic avenues including PharmMapper, Gene Expression Omnibus (GEO) database, protein-protein interaction (PPI) network, and molecular docking. Besides, western blot, CCK-8 transwell analysis of migration and invasion, flow cytometric analysis, and colony formation assays were performed to investigate the underlying mechanism. We found four core nodes (MMP9, JUN, SPP1, and HMOX1) by constructing a PPI network with 23 common targets. Through functional enrichment analysis, we confirmed that the above four target genes are enriched in the TNF, PI3K-AKT, and NF-κB signaling pathways, which are involved in the inflammatory microenvironment and inhibit the development and progression of tumors. Furthermore, molecular docking results demonstrated that quercetin shows strong binding efficiency with the proteins encoded by these 4 key proteins. Finally, quercetin displayed strong antitumor efficacy in PTC cell lines. In this research, we demonstrated the application of network pharmacology in evaluating the mechanisms of action and molecular targets of quercetin, which regulates a variety of proteins and signaling pathways in PTC. These data might explain the mechanism underlying the anticancer effects of quercetin in PTC.

Tourists’ digital footprint: the spatial patterns and development models of rural tourism flows network in Guilin, China

ABSTRACT This paper proposes a network analysis framework for rural tourism flows based on the digital footprint data of Ctrip visitors, and studies network the spatial characteristics and spatial development patterns of rural tourism flows. Research results show the following: (1)rural tourism flow is characterized by large-scale flows between nodes with superior resource endowment conditions and convenient transportation. (2) The overall network structure of rural tourism flow has a low density, and the centrality of rural tourism nodes has an obvious hierarchical structure, and obvious structural hole phenomenon. (3) Rural tourism flow has a significant core-edge structure. (4) The internal space of rural tourism flow exists in several modes, such as core node development, axis development “along traffic arteries” and domain development “clustering groups.” This study addresses the regional imbalance of rural tourism resources and with the intent of boosting the high-quality development of rural tourism destinations.

Alternative Theory of AD Pathogenesis: Membrane Delimitation of the Histone Acetyltransferase Tip60/Kat5

AbstractBackgroundFor decades, mounting genetic and physiological data have pointed to the APP proteolysis as a seminal event in AD pathogenesis. This data has been viewed as supporting the amyloid hypothesis. However, growing data from multiple disciplines disputes that amyloid is a causal mechanism of neurodegeneration. Evidence suggests that FAD mutations are loss of function, inhibiting normal proteolytic processing of APP. APP extracellular and intracellular domains exhibit a broad array of binding interactions. Tip60 (a histone acetyltransferase, also known as Kat5) is an intracellular binding partner that supports the long‐standing speculation that APP functions as a signaling receptor. Recently, a conditional knockout (cKO) of Tip60 demonstrated neurodegenerative pathology and massive reactive gliosis, supporting a role for Tip60 in neurodegeneration. Reports that Tip60 is restricted from the nucleus in AD patient brains bolsters this idea.MethodTo assess the overlap of the cKO Tip60 model and human AD pathogenesis, we compared the dysregulated gene sets (DEGs) in the cKO Tip60 mouse and late‐onset AD postmortem brains. We break DEGs from mouse and human models into AD “biological domains” (BDs), and perform GSEA of GO enrichment within each BD. We then perform downstream analyses using network reconstruction applications and by examining the enriched BDs with pseudotime measures of disease progression in LOAD patients.ResultWe find significant overlap between DEGs in LOAD patients and the Tip60 cKO in multiple BDs, including, mitochondrial metabolism, immune response, structural stabilization, synaptic function, and endolysosomal trafficking. GSEA of LOAD and cKO Tip60 further implicates similar deficits in both models. The network analysis shows a convergence of gene dysregulation at core nodes, further implicating similar processes of dysregulation may impact particularly significant genes in human and mouse models. The pseudotime analysis shows that this dysfunction is an early signature of AD, suggesting Tip60 dysregulation may trigger early‐stage homeostatic destabilization.ConclusionWe propose that aberrant APP proteolysis traps Tip60 at the membrane, preventing the nuclear localization of Tip60, and altering both positive and negative gene regulation that is necessary for neuronal and glial homeostasis, and hypothesize that this is an explanation for the genetic linkage of APP proteolytic processing and AD.

Identification of Important Nodes in Multilayer Heterogeneous Networks Incorporating Multirelational Information

Centrality is an effective method to identify important nodes in complex networks, but it is still a challenge to find influential nodes by making full use of multiple relationships and global network topological features in complex networks. To address these problems, this article proposes an importance identification method for multilayer heterogeneous network node by incorporating multirelational information (MLC). This method studies the relational characteristics of heterogeneous nodes in detail and divides the heterogeneous nodes into different layers according to the node types, which can be further divided into core and auxiliary layers. The importance of the auxiliary layer is quantified by designing the interlayer influence and determining the interlayer influence weights of different connectivity influences; the centrality score of heterogeneous nodes under multiconnectivity relationships is fused using the transmission characteristics of internode relationships in the auxiliary layer, which in turn measures the importance of nodes in the core layer. To evaluate the proposed algorithm, we conduct experiments on five real multilayer heterogeneous networks of different sizes. The results show that MLC can make full use of different types of internode association relationship information, effectively fuse network structure information such as the neighbor weights of core and auxiliary layer nodes, and outperform the existing techniques in identifying important nodes.

Ranking influential spreaders based on both node k-shell and structural hole

The ranking of individual spreaders aims to measure the influential capability of individual nodes and is important to control information spreading in a network. However, many ranking methods are either degree-based, k-shell-related or a combination of the two, which are not necessarily related to influential capability. Inspired by the strengths of the k-shell decomposition method, this work improves it on the basis of structural holes (SH) and proposes a novel ranking method, SHKS. Different from the efforts that aim only to improve the k-shell decomposition method, this work considers the k-shell and SH-based centrality of a node as well as its neighbors and second-order neighbors. Based on the flexible combination of k-shell and SH, SHKS can identify not only the core nodes with large k-shell indices but also the nodes that have small k-shell indices but play an important role in bridging different parts of a network. Experimental results show that SHKS presents better performance than baseline methods in terms of the Kendall τ correlation results, and the average improvements range from 1.3% to 121.1%. SHKS also has the best monotonicity, and its average monotonicity value on experimental networks is close to 0.99. Moreover, SHKS has good performance in identifying the most influential top-k nodes compared with baseline methods.

Wdt-SCAN: Clustering decentralized social graphs with local differential privacy

Social network users scatter geographically, where each maintains a self-centered star graph (i.e., relationships directed related to them). Clustering on such subgraphs can provide valuable insights into the graph structure. Despite its significance, one could not simply collect the unfettered star graph information from users due to privacy concerns. Local differential privacy (LDP), as an emerging privacy model, has been widely adopted in privacy-preserving data collection and analysis. Most existing LDP-based graph analysis methods encode star graphs via adjacency bit vector and suffer from heavy noise due to the sparsity of social networks. Besides, they cluster all nodes indifferently yet ignore that nodes with different degrees are affected differently by noise injection, making the clustering results unsatisfactory. To tackle these issues, we propose Wdt-SCAN, a degree vector based private graph clustering scheme to achieve high quality clustering without compromising individual privacy. Concretely, we design a degree vector encoding model with optimal length to represent star graphs, which reduces the massive noise caused by sparsity. To improve the clustering accuracy, a two-round agglomeration-based decentralized graph clustering method is proposed, which partitions nodes into core nodes and ordinary nodes, and then clusters core nodes to form a graph skeleton as prior knowledge to alleviate the impact of noise injection on ordinary nodes. Theoretical analysis and experiments on real-world datasets show that our proposed method can obtain desirable clustering result while satisfying edge LDP.

Research on Multi-Scale Ecological Network Connectivity-Taking the Guangdong-Hong Kong-Macao Greater Bay Area as a Case Study.

The Guangdong-Hong Kong-Macao Greater Bay Area urban agglomeration is an urban agglomeration with some of the most intensive urbanization since 1980s. A large amount of cultivated land, forest land, water bodies and other land types in the region has been occupied by construction land, resulting in fragmented ecological landscapes and biodiversity in the region and causing many other ecological problems. Based on this, this paper takes the Guangdong-Hong Kong-Macao Greater Bay Area as a case study, constructs an ecological network of the dispersion scale of five species from 1990 to 2020 based on a morphological spatial pattern analysis (MSPA) method, identifies the ecological groups in the network and uses the core node-based community evolution path tracking algorithm to analyze the ecological groups in order to explore the changes of ecological network connectivity at different scales in the region and to reveal the overall and local characteristics and changes of the migratory space of terrestrial mammals with different dispersion capabilities. The research results show that: (1) From 1990 to 2020, the area of construction land in the Guangdong-Hong Kong-Macao Greater Bay Area increased sharply, with good connectivity in the northwest, southwest and eastern regions and poor connectivity in the central region. (2) There are obvious differences between the overall and local changes in the connectivity trends of multi-scale regional ecological networks. On the whole, the overall ecological connectivity of the ecological network at each scale showed a gradual upward trend, and the overall connectivity index IIC and the possible connectivity index PC gradually increased with the increase of the maximum dispersal distance of species. From the perspective of local patches, the larger the species dispersion scale, the larger the value of the revised betweenness centrality index and the patch possible connectivity index. (3) The distribution of ecological groups at different species dispersion scales is different, and the smaller the dispersal scale of the species, the greater the distribution of ecological groups. Small-scale species are limited by the maximum dispersal distance, and the range of their ecological groups is generally small. Small-scale (3 km), mesoscale (10 km) and large-scale (30 km) core nodes of ecological groups show a gradual increase trend, and the overall connectivity of ecological groups has improved. However, the core nodes of the extra-large-scale (60 km) and ultra-large-scale (100 km) ecological groups show a trend of decreasing fluctuations, and the overall connectivity within the ecological group has declined. This study is helpful to clarify the structural characteristics of regional ecological space and provide a theoretical basis for regional ecological planning.

SGM: Strategic Game Model for Resisting Node Misbehaviour in IoT-Cloud Ecosystem

This paper introduces a computational strategic game model capable of mitigating the adversarial impact of node misbehaviour in large-scale Internet of Things (IoT) deployments. This security model’s central concept is to preclude the participation of misbehaving nodes during the routing process within the ad hoc environment of mobile IoT nodes. The core of the design is a simplified mathematical algorithm that can strategically compute payoff embrace moves to maximise gain. At the same time, a unique role is given to a node for restoring resources during communication or security operations. Adopting an analytical research methodology, the proposed model uses public and private cloud systems for integrating quality service delivery with secure agreements using a Global Trust Controller and core node selection controller to select an intermediate node for data propagation. The initiation of the game model is carried out by identifying mobile node role followed by choosing an optimal payoff for a normal IoT node. Finally, the model leads to an increment of gain for selecting the regular IoT node for routing. The findings of the evaluation indicate that the proposed scheme offers 36% greater accuracy, 25% less energy, 11% faster response time, and 27% lower cost than the prevalent game-based models currently used to solve security issues. The value added by the proposed study is the simplified game model which balances both security demands and communication demands.