Core Nodes Research Articles

Group Sparse β -Model for Network

Sparsity, homogeneity and heterogeneity are three important characteristics of many real-life networks. The recently proposed Sparse β -Model divides nodes into core ones and peripheral ones to accommodate sparsity, but the parameters of core nodes are assumed to be of similar magnitude, which may not be in line with applications. In this paper, we propose the Group Sparse β -Model that splits the core nodes into groups and assumes different orders of magnitude of parameters in different groups, accounting for the heterogeneity among core nodes. When the groups are known, we provide consistent and asymptotically normal moment estimators of the parameters that control the global and local density. Based on that, consistency and asymptotic normality of the maximum likelihood estimators of the remaining parameters are derived. We also establish finite-sample error bounds results. When the groups are unknown, a ratio method is proposed to detect groups, which is computationally efficient. Simulations show competitive results and the analysis of a corporate inter-relationships network illustrates the usefulness of the proposed model.

The research of knowledge diffusion network model for Tourism Destination-Public ecological civilization.

Visitor education plays a crucial role in the knowledge diffusion process in outdoor recreation and nature-based tourism. It entails sharing information, experiences, and insights with visitors to enhance their understanding and appreciation of the natural environment. Our methodology for investigating the diffusion of ecological civilization knowledge in tourism destinations involves constructing a knowledge diffusion network model. In this model, scenic spots, tourists, and the public are defined as network nodes, with the communication channels between them representing the edges of the network. By constructing a scale-free complex network, the knowledge diffusion mode of scenic spots can be depicted. The layer of resource supply node consists of different scenic spots, forming the core nodes set for knowledge diffusion in the tourism industry. This research aims to further explore the social and economic value of the tourism areas' ecological civilization knowledge diffusion, as well as analyze the path, quantity, and cost of knowledge diffusion. by analyzing this knowledge diffusion network model, insights into the effectiveness and impact of visitor education in promoting ecological civilization and sustainable practices in tourism destinations can be gained. Overall, this approach provides a theoretical framework for investigating and comprehending the knowledge diffusion process in Tourism Destination-Public ecological civilization, thereby shedding light on the social and economic benefits that can be derived from sustainable tourism practices.

Characterization of Industrial Carbon Emission Dynamics and Network Structure Evolution in China

Given that industry is the major source of carbon emissions, clarifying the carbon transfer regularity triggered by industry production linkage is of great importance in realizing the synergistic development of industry to reduce pollution and carbon emissions. Based on the perspective of responsibility allocation, the study accounted for the industry carbon emissions and constructed a carbon transfer weighted directed network model and applied the social network analysis method to explore the structure of the industry carbon transfer network and its evolution characteristics from 2005 to 2020. The results showed that： ① The overall carbon emissions of industries from 2005 to 2020 presented a rapid growth trend and large differences occurred in emissions between industries. ② The scale of the carbon transfer network was expanding rapidly and the characteristics of the small-world network were significant. ③ The community structure of the carbon transfer network was obvious, including four optimal associations： high carbon emission, carbon transmission, carbon endogenous, and high spillover. ④ The construction industry and the metal smelting and rolling processing industry were the core of the carbon transfer network structure, with strong network control. In view of the above characteristics, we proposed countermeasures and suggestions in terms of implementing the allocation of carbon emission reduction responsibilities and differentiated emission reduction measures for associations as well as focusing on the management of core nodes of the network, with the intent of providing cross-sectoral synergistic emission reduction ideas for the development of sustainable low-carbon transformation in China's industry sector.

Construction of Metaphorical Maps of Cyberspace Resources Based on Point‐Cluster Feature Generalization

ABSTRACTIn the digital age, the expansion of cyberspace has resulted in increasing complexity, making clear cyberspace visualization crucial for effective analysis and decision‐making. Current cyberspace visualizations are overly complex and fail to accurately reflect node importance. To address the challenge of complex cyberspace visualization, this study introduces the integrated centrality metric (ICM) for constructing a metaphorical map that accurately reflects node importance. The ICM, a novel node centrality measure, demonstrates superior accuracy in identifying key nodes compared to degree centrality (DC), k‐shell centrality (KC), and PageRank values. Through community partitioning and point‐cluster feature generalization, we extract a network's hierarchical structure to intuitively represent its community and backbone topology, and we construct a metaphorical map that offers a clear visualization of cyberspace. Experiments were conducted on four original networks and their extracted backbone networks to identify core nodes. The Jaccard coefficient was calculated considering the results of the three aforementioned centrality measures, ICM, and the SIR model. The results indicate that ICM achieved the best performance in both the original networks and all extracted backbone networks. This demonstrates that ICM can more precisely evaluate node importance, thereby facilitating the construction of metaphorical maps. Moreover, the proposed metaphorical map is more convenient than traditional topological maps for quickly comprehending the complex characteristics of networks.

Network Analysis of Association Between Problematic Social Network Use and Alexithymia in Freshmen.

Exploring the core and bridge nodes in problematic social network use and alexithymia among freshmen to provide a basis for understanding the relationship and interventions. A total of 4057 first-year students from four universities in Shandong Province were chosen and surveyed with the Problematic Mobile Social Media Use Assessment Questionnaire and the Toronto Alexithymia Scale (TAS). Network analysis was performed using R to estimate the connections between nodes. Centrality and predictability indicators were used to identify key nodes, with accuracy and stability validation techniques applied. Gender and residence differences in the network structure were also examined. In the problematic social network use network, the nodes with the highest expected influence were P16 (excessive swiping) and P14 (lack of control over phone usage). In the problematic social network use-alexithymia network, cognitive failure had the highest strength (strength = 1.155) and centrality. Difficulty identifying feelings (bridgestrength = 0.32), externally oriented thoughts (bridgestrength = 0.24), and cognitive failure (bridgestrength = 0.19) were key bridge nodes. No significant differences were found in the network structure across gender and residence, though the network was tightly connected. Cognitive failure plays a central role in problematic social network use among freshmen. Difficulty identifying feelings, externally oriented thoughts, and cognitive failure are critical in linking problematic social network use with alexithymia.

Regulatory network analysis reveals gene-metabolite relationships in pear fruit treated with methyl jasmonate

The economic value of pear is determined by its intrinsic qualities, which are influenced by metabolites produced during the ripening process. Methyl jasmonate (MeJA), a hormone, plays an important role in plant metabolism. To date, few studies have investigated the molecular mechanism underlying the changes in metabolic pathways related to the internal quality of pear fruit after MeJA treatment. In this study, ultrahigh-performance liquid chromatography‒Q Exactive Orbitrap mass spectrometry (UHPLC‒QE‒MS) was used to determine the changes in metabolite contents in pear after MeJA treatment. MeJA treatment primarily activated carbohydrate metabolism and amino acid metabolism pathways. Through combined analysis of UHPLC‒QE‒MS data and whole-transcriptome data, the abovementioned pathways and each metabolite were analysed separately, and competitive endogenous RNA (ceRNA) and microRNA–transcription factor–target (miRNA–TF–target) regulatory networks were constructed. The core nodes of three genes (PEA, Pbr022732.1; GAA, Pbr035655.1; and miR8033-x) and two genes (SDS, Pbr031708.1; and novel-m6796-3p) were associated with the carbohydrate metabolism and amino acid metabolism pathways, respectively. The core mRNA nodes TCONS_00048038 and Pbr019584.1, the core miRNA node miR4993-x, the core lncRNA node TCONS_0004356, the core circRNA node novel_circ_001967 and the core transcription factor node TSO1 (Pbr025407.1) were identified via separate metabolite analyses. These findings elucidate the changes in metabolites related to fruit quality in ‘Nanguo’ pear and the relationships between the metabolites and genes, reveal the molecular mechanism underlying the response of MeJA treatment in pear fruit, and provide a theoretical basis for improving the internal quality of ‘Nanguo’ pear.

Static Resilience Evolution of the Global Wood Forest Products Trade Network: A Complex Directed Weighted Network Analysis

This paper analyzes the static resilience of global wood forest products trade networks across upstream, midstream, downstream, and recycling sectors using a complex directed weighted network approach. By examining topological features and resilience from 2002 to 2021, this study reveals significant structural evolution and scale expansion in these networks. It finds improvements in network efficiency and resilience, alongside an increase in weighted hierarchy highlighting the prominent roles of core countries like China, the US, and Germany. While these countries bolster network resilience, they also introduce certain vulnerabilities. This study finds notable disassortative mixing without trade volume weights and diversified trends with weights, offering new insights into network dynamics. Core nodes must address disruption risks, enhance diversity, and establish emergency response mechanisms. In the recycling sector, this paper highlights weak trade connections and low resilience, with the US maintaining dominance, China’s influence waning, and India’s rapid ascent. This paper concludes by emphasizing the need for refined indicator systems and deeper explorations into resilience enhancement strategies for operational and targeted suggestions.

Revolutionizing MANET Route Discovery with INTSM: An Innovative Load Balancing Approach

Communication challenges in ad hoc networks arise due to the mobility of nodes, causing frequent changes in connections and locations. Maintaining network equilibrium to prevent node overload and underutilization is crucial. However, imposing static behaviors on nodes to improve performance can lead to delays, especially in core nodes. Addressing these issues, this research proposes the Intermediate Node Traffic Sharing Model (INTSM) for ad hoc networks. INTSM prioritizes congestion control and load balancing during route discovery, aiming to optimize network resource utilization and traffic distribution, thereby reducing packet delays. The model employs dynamic traffic sharing algorithms that consider real-time network conditions, enabling nodes to adjust their behaviour adaptively. This approach minimizes congestion by distributing traffic loads more evenly across the network, preventing bottlenecks at central nodes. Additionally, INTSM incorporates predictive analysis to foresee potential congestion points and reroute traffic proactively, enhancing overall network stability and performance. Extensive simulations demonstrate that INTSM significantly reduces average packet delay and improves throughput compared to traditional routing protocols. The results highlight the model's efficacy in diverse scenarios, including high mobility and varying traffic loads, proving its robustness and scalability. The primary objective of this study is to enhance navigation and equilibrium mechanisms to improve the performance of ad hoc networks, contributing to more reliable and efficient wireless communication systems. The findings of this research have significant implications for the design of future ad hoc networks, particularly in applications requiring high reliability and quick adaptation to changing network conditions, such as disaster recovery, military operations, and mobile sensor networks. By addressing the critical challenges of congestion control and load balancing, INTSM offers a promising solution to enhance the resilience and efficiency of ad hoc networks.

Divergent pattern of functional connectivity within the dorsal attention network differentiates schizophrenia and bipolar disorder patients.

Schizophrenia (SZ) and bipolar disorder (BD) share common clinical features, symptoms, and neurocognitive deficits, which results in common misdiagnosis. Recently, it has been suggested that alterations within brain networks associated with perceptual organization yield potential to distinguish SZ and BD individuals. The aim of our study was to evaluate whether functional connectivity (FC) of the dorsal attention network (DAN) may differentiate both conditions. The study involved 90 participants: 30 remitted SZ patients, 30 euthymic BD patients, and 30 healthy controls (HC). Resting state functional magnetic resonance imaging was used to compare the groups in terms of the FC within the core nodes of the DAN involving frontal eye fields (FEF) and intraparietal sulcus (IPS). BD patients presented weaker inter-hemispheric FC between right and left FEF than HC. While SZ did not differ from HC in terms of inter-FEF connectivity, they presented increased inter- and intra-hemispheric FC between FEF and IPS. When compared with BD, SZ patients showed increased FC between right FEF and other nodes of the network (bilateral IPS and left FEF). We have shown that altered resting state FC within DAN differentiates BD, SZ, and HC groups. Divergent pattern of FC within DAN, consisting of hypoconnectivity in BD and hyperconnectivity in SZ, might yield a candidate biomarker for differential diagnosis between both conditions. More highly powered studies are needed to confirm these possibilities.

Which comes first? Comorbidity of depression and anxiety symptoms: A cross-lagged network analysis

Depression and anxiety significantly impact college students, leading to various negative outcomes. While numerous studies have investigated the relationship between these two conditions, their temporal sequence remains unresolved. Many previous studies have concentrated on broad latent variables, often neglecting the nuanced symptomatology perspective, which may offer deeper insight into the clinical characteristics of these disorders. In this study, we collected questionnaire data from a college in South China using a cluster random sampling method. Data collection occurred over two time points, with the first round completed in November 2022 and May 2023, with a six-month interval. A total of 689 participants successfully completed the questionnaires during both rounds. Employing cross-lagged network analysis from a symptom-focused perspective, this research examines the interactions and predictive relationships between symptoms of depression and anxiety. The findings identified key symptoms-specifically “Irritability”, “Guilty” and “Sad mood"- as critical bridging nodes of connection within the depression and anxiety symptom network. Our analysis revealed both bidirectional predictive relationships between certain symptoms nodes of depression and anxiety, as well as unidirectional ones. By highlighting these core nodes and their directional relationships, this study offers valuable insights that can inform targeted intervention and treatment strategies for enhancing mental health among college students.

Progressive gray matter atrophy in parkinsonian variant of multiple system atrophy assessed by using causal structural covariance network.

Multiple system atrophy (MSA), a rare neurodegenerative disease, is usually accompanied by brain morphological alterations. However, the causal relationships between progressive gray matter atrophy in MSA parkinsonian (MSA-P) subtype remain unknown. In total, thirty-five MSA-P patients and thirty-five healthy controls (HC) underwent three-dimensional high-resolution T1-weighted structural imaging and voxel-based morphometry analysis. The causal structural covariance network (CaSCN) of gray matter was assessed to explore the causal relationships in MSA-P. With greater illness duration, the reduction of gray matter was originated from right cerebellum and progressed to bilateral cerebellum, fusiform gyrus, insula, putamen, caudate nucleus, frontal lobe, right angular gyrus, right precuneus, left middle occipital lobe and left inferior temporal lobe, then expanded to midbrain, bilateral para-hippocampus, thalamus, temporal lobe, inferior parietal lobule (IPL), precentral gyrus, postcentral gyrus and middle cingulate cortex. The right cerebellum was revealed to be the core node of the directional network and projected positive causal effects to bilateral cerebellum, caudate nucleus and left IPL. MSA-P patients showed progression of gray matter atrophy over time, with the right cerebellum probably as a primary hub. Furthermore, the early structural vulnerability of cerebellum in MSA-P may play a pivotal role in the modulation of motor and non-motor circuits at the structural level.

Research on Location Optimization of Logistics Center Based on Center of Gravity Method: A Case Study of Jilin Province

With the rapid development of the global economy, logistics plays an increasingly important role in modern society. As the core node of the logistics system, logistics center is responsible for the distribution, transfer and distribution of goods. The scientific and rational location of the logistics center will directly affect the efficiency and cost of logistics transportation. In this paper, Jilin Province, a key agricultural producing area in China, is selected as the research object. By collecting certain historical data information, regression analysis method and moving average method are used to predict the grain output of each city, and the average value of the predicted values obtained by the two methods is used as the final forecast value. The initial site selection of grain logistics center in Jilin Province is calculated by using the center of gravity, and the new site selection after iteration is obtained by using the center of gravity. Finally, the location of the final grain logistics center is determined by considering the factors such as geographical location, traffic accessibility and usable area around. This study can provide some reference value for the optimization of logistics center location.

Differences in networks of alexithymia and cognitive emotion regulation strategies among older adults with different health statuses in eastern China—A network analysis and network comparison

To identify core and bridge nodes in the network structure of alexithymia and cognitive emotion regulation strategies in community-dwelling older adults, and compare network differences among older adults with different health statuses, we recruited 677 participants and network analysis was performed in R 4.2.0. After including the covariates, the nodes Catastrophizing, Difficulty Identifying feelings, and Refocusing on Planning ranked as the top three. The nodes Externally Oriented Thoughts and Difficulty Identifying Feelings were identified as bridge nodes based on bridge strength values. Significant differences were observed between the healthy and comorbidity groups, and also between the single chronic disease and comorbidity groups (p < 0.05). Catastrophizing, Difficulty Identifying Feelings, and Refocusing on Planning were the core nodes, and Externally Oriented Thoughts and Difficulty Identifying Feelings were the key bridge nodes. The network structure of comorbidity in older adults was characterized by stronger ties to non-adaptive cognitive emotion regulation strategies.

Estimation of the injection criteria for magnetic hyperthermia therapy based on tumor morphology

Intratumoral multi-injection strategy enhances the efficacy of magnetic nanoparticle hyperthermia therapy (MNPH). In this study, criteria for the selection of injections and their location depending on the tumor shape/geometry are developed. The developed strategy is based on the thermal dosimetry results of different invasive 3D tumor models during MNPH simulation. MNPH simulations are conducted on physical tumor tissue models encased within healthy tissue. The tumor shapes are geometrically divided into a central tumor region containing maximum tumor volume and a peripheral tumor portion protruding in any random direction. The concepts of core and invasive radius are used to geometrically divide the tumor volume. Primary & secondary injections are used to inject MNP fluid into these respective tumor regions based on the invasiveness of the tumor. The optimization strategy is devised based on the zone of influence of primary & secondary injection. Results indicate that the zone of influence of secondary injection lies between 0.7 and 0.8 times the radial distance between the center of the tumor core and branch node point (extreme far endpoint on the invasive tumor surface). Additionally, the multi-injection strategy is more effective when the protrusion volume exceeds 10% of the total volume. The proposed algorithm is used to devise multi-injection strategies for arbitrarily shaped tumors and will assist in pre-planning magnetic nanoparticle hyperthermia therapy.

Resilience evaluation and enhancing for China’s electric vehicle supply chain in the presence of attacks: A complex network analysis approach

Over the past decade, the electric vehicle (EV) industry has experienced rapid growth, necessitating a deeper understanding of its supply chain network’s structure and resilience. This study presents an analysis into China’s EV supply chain network through a two-tier network, employing complex network approach to explore its topological characteristics and network’s resilience. The results reveal that the nodes within China’s EV network follow a power-law distribution, with shorter average path lengths compared to other automobile networks, indicating a concentration of connections upstream. The resilience assessment shows that while the network is resilient against random attacks, it is significantly vulnerable to targeted attacks. Importantly, the High-Betweenness Addition (HBA) strategy is identified as a better strategy for improving the network’s overall resilience. This strategy of reinforcing the centrality of core nodes within the supply chain is highlighted as a crucial approach for enhancing the network’s resilience. Our findings underscore the importance of developing strategic connections among high-betweenness nodes to distribute these enhancements evenly, aiming to add one or two edges for enhancing network resilience. This study offers a lucid roadmap for enhancing the resilience of China’s EV supply chain network in the face of emerging challenges.

Local core expanding-based label diffusion and local deep embedding for fast community detection algorithm in social networks

Community detection is a key task in social network analysis, as it reveals the underlying structure and function of the network. Various global and local techniques exist for uncovering community structures in social networks wherein diffusion-based algorithms are proposed as novel methods for local community detection, particularly suited for large-scale networks. The efficacy of diffusion processes and initial detection is paramount in the successful identification of community structures within social networks. This effectiveness hinges significantly on the meticulous selection of the label diffuser core, which serves as the foundation for propagating labels through the network, and the precise labeling of boundary nodes. Addressing the constraints of current community detection algorithms, notably their time complexity and efficiency, this paper proposes a novel local community detection algorithm that combines core expansion with label diffusion, and deep embedding techniques. In the proposed method, a new centrality measure is introduced for appropriate core selection to facilitate precise label diffusion in the initial phase. Subsequently, a deep embedding technique is employed for updating labels of boundary and core nodes using the GraphSage embedding method. Finally, a rapid merging step is executed to amalgamate initially proximate communities into finalized community structures in large-scale social networks. We evaluate our algorithm on 14 real-world and 4 synthetic networks and show that it outperforms existing methods in terms of NMI, F-measure, ARI, and modularity. According to numerical results, the proposed method shows approximately 1.04 %, 1.03 %, and 1.12 % improvement in F-measure, NMI, and ARI measures respectively, compared to the second-best method, LBLD, in the networks with ground-truth. In addition, our method is able to accurately identify communities in large-scale networks such as Orkut, YouTube, and LiveJournal, where it ranks among the top-performing methods. Our approach exhibits the best performance in terms of ARI compared to other algorithms under comparison.

Therapeutic DBS for OCD Suppresses the Default Mode Network.

Deep brain stimulation (DBS) of the anterior limb of the internal capsule (ALIC) is an emerging treatment for severe, refractory obsessive-compulsive disorder (OCD). The therapeutic effects of DBS are hypothesized to be mediated by direct modulation of a distributed cortico-striato-thalmo-cortical network underlying OCD symptoms. However, the exact underlying mechanism by which DBS exerts its therapeutic effects still remains unclear. In five participants receiving DBS for severe, refractory OCD (3 responders, 2 non-responders), we conducted a DBS On/Off cycling paradigm during the acquisition of functional MRI to determine the network effects of stimulation across a variety of bipolar configurations. We also performed tractography using diffusion-weighted imaging (DWI) to relate the functional impact of DBS to the underlying structural connectivity between active stimulation contacts and functional brain networks. We found that therapeutic DBS had a distributed effect, suppressing BOLD activity within regions such as the orbitofrontal cortex, dorsomedial prefrontal cortex, and subthalamic nuclei compared to non-therapeutic configurations. Many of the regions suppressed by therapeutic DBS were components of the default mode network (DMN). Moreover, the estimated stimulation field from the therapeutic configurations exhibited significant structural connectivity to core nodes of the DMN. Therapeutic DBS for OCD suppresses BOLD activity within a distributed set of regions within the DMN relative to non-therapeutic configurations. We propose that these effects may be mediated by interruption of communication through structural white matter connections surrounding the DBS active contacts.

Stage-dependent differential impact of network communication on cognitive function across the continuum of cognitive decline in Parkinson's disease

ObjectiveOur objective was to explore the patterns of resting-state network (RSN) connectivity alterations and investigate how the influences of individual-level network connections on cognition varied across clinical stages without assuming a constant relationship. Methods108 PD patients with continuum of cognitive decline (PD-NC = 46, PD-MCI = 43, PDD = 19) and 34 healthy controls (HCs) underwent resting-state functional MRI and neuropsychological tests. Independent component analysis (ICA) and graph theory analyses (GTA) were employed to explore RSN connection changes. Additionally, stage-dependent differential impact of network communication on cognitive performance were examined using sparse varying coefficient modeling. ResultsCompared to HCs, the dorsal attention network (DAN) and dorsal sensorimotor network (dSMN) were central networks with decreased connections in PD-NC and PD-MCI stage, while the lateral visual network (LVN) emerged as a central network in patients with dementia. Additionally, connectivity of the cerebellum network (CBN) increased in the PD-NC and PD-MCI stages. GTA demonstrated decreased nodal metrics for DAN and dSMN, coupled with an increase for CBN. Moreover, the degree centrality (DC) values of DAN and dSMN exhibited a stage-dependent differential impact on cognitive performance across the continuum of cognitive decline. ConclusionOur findings suggest that across the progression of cognitive impairment, the LVN gradually transitions into a core node with reduced connectivity, while the enhancement of connections in CBN diminishes. Furthermore, the non-linear relationship between the DC values of RSNs and cognitive decline indicates the potential for tailored interventions targeting specific stages.

Integrin αM promotes macrophage alternative M2 polarization in hyperuricemia-related chronic kidney disease.

Hyperuricemia is an essential risk factor in chronic kidney disease (CKD), while urate-lowering therapy to prevent or delay CKD is controversial. Alternatively activated macrophages in response to local microenvironment play diverse roles in kidney diseases. Here, we aim to investigate whether and how macrophage integrin αM (ITGAM) contributes to hyperuricemia-related CKD. In vivo, we explored dynamic characteristics of renal tissue in hyperuricemia-related CKD mice. By incorporating transcriptomics and phosphoproteomics data, we analyzed gene expression profile, hub genes and potential pathways. In vitro, we validated bioinformatic findings under different conditions with interventions corresponding to core nodes. We found that hyperuricemia-related CKD was characterized by elevated serum uric acid levels, impaired renal function, activation of macrophage alternative (M2) polarization, and kidney fibrosis. Integrated bioinformatic analyses revealed Itgam as the potential core gene, which was associated with focal adhesion signaling. Notably, we confirmed the upregulated expression of macrophage ITGAM, activated pathway, and macrophage M2 polarization in injured kidneys. In vitro, through silencing Itgam, inhibiting p-FAK or p-AKT1 phosphorylation, and concurrent inhibiting of p-FAK while activating p-AKT1 all contributed to the modulation of macrophage M2 polarization. Our results indicated targeting macrophage ITGAM might be a promising therapeutic approach for preventing CKD.

Sampling unknown large networks restricted by low sampling rates

Graph sampling plays an important role in data mining for large networks. Specifically, larger networks often correspond to lower sampling rates. Under the situation, traditional traversal-based samplings for large networks usually have an excessive preference for densely-connected network core nodes. Aim at this issue, this paper proposes a sampling method for unknown networks at low sampling rates, called SLSR, which first adopts a random node sampling to evaluate a degree threshold, utilized to distinguish the core from periphery, and the average degree in unknown networks, and then runs a double-layer sampling strategy on the core and periphery. SLSR is simple that results in a high time efficiency, but experiments verify that the proposed method can accurately preserve many critical structures of unknown large scale-free networks with low sampling rates and low variances.