Network Structure Research Articles

Effect of Carbon Black Content and Firing Atmosphere on the Properties and Microstructure of Al2O3-SiC-C Castables

Al2O3-SiC-C (ASC) castables containing spherical asphalt are widely utilized in high-temperature metallurgical furnaces because of their good abrasive resistance and slag resistance; however, the release of hazardous benzopyrene during the pyrosis process in spherical asphalt is detrimental to the environment and to the health of furnace workers. Herein, nontoxic nano carbon black (CB) was selected as the carbon source for ASC castables, and the effects of the CB amount and sintering atmosphere on the properties of ASC castables were investigated in this work. The results show that on increasing CB from 0.5% to 2%, the cold strength of the samples after firing in the reducing atmosphere increased, the residual strength increased, and the slag penetrated depth decreased; the reasons can be ascribed to nano CB being able to fill the pores to reduce the apparent porosity of the castables. Furthermore, SiC whiskers were formed at elevated temperatures and generated a network structure, which was beneficial in improving their properties. When CB was 1%, the cold modulus of the rupture of the samples after firing in the oxidizing atmosphere and reducing atmosphere were higher (about 20 MPa), the retained strength ratio of the samples pre-fired in the reducing atmosphere was the highest (85.4%), the hot strength at 1400 °C of the samples tested in the oxidized atmosphere was the highest (5.3 MPa), and the slag resistance of the samples measured in the oxidizing atmosphere was the best. The castables heat-treated in the air atmosphere possessed higher hot strength and slag resistance; the reasons can be attributed to the formed SiO2 derived from the oxidation of SiC, which reacted with Al2O3 to form mullite, creating a strengthening effect and decreasing the porosity and increasing the viscosity of slag, thereby improving the hot strength and slag resistance.

Abstract 4121198: Structural Network Alterations After Cardiopulmonary Bypass In a Juvenile Porcine Model

Background: Cardiopulmonary bypass (CPB) can trigger inflammatory and oxidative stresses in the developing brain. Clinical studies showed altered brain network structure in children after infant cardiac surgery. However, the impact of CPB on structural connectivity remains poorly understood. This study aims to determine the effects of CPB on structural connectivity and underlying cellular events using a juvenile porcine model. Methods: CPB (150 min total) was performed on 2-week-old Yorkshire piglets. High-resolution diffusion tensor imaging (DTI) and neurite orientation dispersion and density (NODDI) imaging were performed 4 weeks post-CPB in a total of 12 animals (Control; n=7, CPB; n=5). Structural connectivity assay together with histological analysis was conducted to assess the brain network organization. Results: Our network analysis (Image 1) revealed a CPB-induced reduction of structural connections in particular nodes, such as in the anterior prefrontal cortex (APC), primary somatosensory cortex, substantia nigra (SN), and globus pallidus (GP). In contrast, there was an increase in the premotor cortex (PreMC), primary motor cortex (PriMC), dorsal posterior cingulate cortex (DPCC), and somatosensory cortex (SAC). Three-dimensional images were generated based on these altered nodes, and tractography was generated to indicate changed fiber tracking (Image 2). We found reduced network connections between the basal ganglia and frontal cortex, and increased connections between the motor and posterior region. DTI and NODDI indicated that microstructures of the superior corona radiata (SCR), internal capsule (PLIC), and anterior prefrontal (AP-WM), motor (PreM-/PriM-WM), and somatosensory white matter (SA-WM) were significantly altered after CPB (Image 3). While Iba1+ microglia was activated across all brain regions after CPB (P<.05), CPB-induced oligodendrocyte immatureness was identified in the WMs involved in APC-SN and APC-GP pathways (P<.05). Notably, CPB increased the number of mature oligodendrocytes in motor and SA-WMs (P<.05) where structural connections were increased after CPB. In addition, we found SN dopaminergic neuron injury after CPB (P<.05). Conclusion: CPB altered structural network organization in the developing brain. Spatial differences in oligodendrocyte responses and susceptibility of specific subpopulations of neurons may contribute to the unique connectivity alterations observed after CPB.

Early Strength and Microscopic Mechanisms of Alkali-Metal Hydroxide-Activated Tungsten Tailings

The excellent mechanical properties of alkaline-activated tailings are essential for their increased use in building materials. While numerous studies have been conducted on activated tailings, the strength of alkaline-activated tungsten slag has not been extensively explored due to the low reactivity of silicon and aluminum in these tailings. This research delves into the early unconfined compressive strength of tungsten tailings activated by two alkali solutions (NaOH and KOH) at three different alkali concentrations (mass ratio of alkali to tungsten tailings), cured at 80 °C over periods of one day, three days, and seven days. The study finds significant improvements in the stability of tungsten tailings when forming (C, N)-A-S-H or (C, K)-A-S-H gels with both alkalis. Scanning Electron Microscope (SEM) results show that the morphology of the (C, N)-A-S-H gels transitions from membranous to flocculated and then to a three-dimensional network as the NaOH content and curing time increase. Conversely, the (C, K)-A-S-H gels primarily exhibit thin-film morphology with some three-dimensional network structures. The presence of flocculation and three-dimensional mesh in the gels fosters the formation of a robust skeletal structure, enhancing the strength of the samples. Furthermore, specimens treated with NaOH solution exhibit a higher gel content compared to those treated with KOH solution. These factors contribute to the superior efficacy of sodium hydroxide in enhancing the strength of tungsten tailings compared to potassium hydroxide. X-ray Diffraction (XRD) and Fourier Transform Infrared Spectroscopy (FTIR) results identify the formation of new phases such as pirssonite, buetschliite, potassium bicarbonate, and potassium carbonate. The first new phase results from the carbonization of excess NaOH solution, while the latter phases arise from the carbonization of excess KOH solution. These carbonization processes negatively impact the strength of the materials.

Recent Development of Fibrous Hydrogels: Properties, Applications and Perspectives.

Fibrous hydrogels (FGs), characterized by a 3D network structure made from prefabricated fibers, fibrils and polymeric materials, have emerged as significant materials in numerous fields. However, the challenge of balancing mechanical properties and functions hinders their further development. This article reviews the main advantages of FGs, including enhanced mechanical properties, high conductivity, high antimicrobial and anti-inflammatory properties, stimulus responsiveness, and an extracellular matrix (ECM)-like structure. It also discusses the influence of assembly methods, such as fiber cross-linking, interfacial treatments of fibers with hydrogel matrices, and supramolecular assembly, on the diverse functionalities of FGs. Furthermore, the mechanisms for improving the performance of the above five aspects are discussed, such as creating ion carrier channels for conductivity, in situ gelation of drugs to enhance antibacterial and anti-inflammatory properties, and entanglement and hydrophobic interactions between fibers, resulting in ECM-like structured FGs. In addition, this review addresses the application of FGs in sensors, dressings, and tissue scaffolds based on the synergistic effects of optimizing the performance. Finally, challenges and future applications of FGs are discussed, providing a theoretical foundation and new insights for the design and application of cutting-edge FGs.

Ester-Modified Sodium Silicate Grout Material for Moraine Stabilization: Synthesis and Freeze-Thaw Resistance

To achieve effective consolidation of fine particles in moraine and enhance the freeze-thaw resistance of the consolidated body, this study developed a novel grouting material using sodium silicate, lipid-based curing agents, and acidic catalysts. The gelation time and rheological properties of this material were tested. The freeze-thaw resistance was studied through changes in uniaxial compressive strength (UCS) after freeze-thaw cycles, while the consolidation mechanism was analyzed using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). The experimental results indicate that the material’s gelation time can be controlled between 30s and 1600s, with an initial viscosity ranging from 5.9 to 9.8 mPa·s. Predictive models for these two indicators were established, and variance analysis revealed the influence ranking for gelation time: phosphoric acid dosage had the greatest effect, followed by EGDA content, with the Baume degree of sodium silicate having the least effect. The initial viscosity positively correlated with the Baume degree of sodium silicate and exhibited exponential growth over time. EGDA addition enhanced UCS by over 450%, reaching 1.2 MPa. During freeze-thaw cycles, strength degradation of the consolidated body was reduced by 10% to 30%. Microstructural tests showed that EGDA promotes silica gel formation and creates a network structure with unreacted sodium silicate, forming a dense consolidated body with moraine fine particles, thereby enhancing freeze-thaw resistance. These findings provide design references and theoretical support for moraine grouting in cold regions.

AER-Net: Attention-Enhanced Residual Refinement Network for Nuclei Segmentation and Classification in Histology Images

The acurate segmentation and classification of nuclei in histological images are crucial for the diagnosis and treatment of colorectal cancer. However, the aggregation of nuclei and intra-class variability in histology images present significant challenges for nuclei segmentation and classification. In addition, the imbalance of various nuclei classes exacerbates the difficulty of nuclei classification and segmentation using deep learning models. To address these challenges, we present a novel attention-enhanced residual refinement network (AER-Net), which consists of one encoder and three decoder branches that have same network structure. In addition to the nuclei instance segmentation branch and nuclei classification branch, one branch is used to predict the vertical and horizontal distance from each pixel to its nuclear center, which is combined with output by the segmentation branch to improve the final segmentation results. The AER-Net utilizes an attention-enhanced encoder module to focus on more valuable features. To further refine predictions and achieve more accurate results, an attention-enhancing residual refinement module is employed at the end of each encoder branch. Moreover, the coarse predictions and refined predictions are combined by using a loss function that employs cross-entropy loss and generalized dice loss to efficiently tackle the challenge of class imbalance among nuclei in histology images. Compared with other state-of-the-art methods on two colorectal cancer datasets and a pan-cancer dataset, AER-Net demonstrates outstanding performance, validating its effectiveness in nuclear segmentation and classification.

Mining teacher informal online learning networks: Community commitment in unstructured learning environments

AbstractBackgroundSocial media provides new opportunities for teachers to learn, communicate and develop professional relationships. It has been proved to be a valid and helpful resource for teachers' professional learning purposes.ObjectivesWhile previous studies pursued questions like how participants feel, how to support interaction and why participants remain committed, we asked a more fundamental question: what is the structure of a massive informal online professional learning network and what dynamics can we expect regarding participants' commitment?MethodsThis work presents an empirical study of massive informal online professional networks to investigate the dynamics of learning communities and participants' commitment over time. We employed social network analysis and data mining techniques on a longitudinal data set of more than 400,000 tweets published with the hashtag “#edchat.”Results and ConclusionsWe found that around 30% of participants remained committed to the informal learning community over time. Meanwhile, as more and more people committed to the online learning community, participants tended to form smaller communities where the internal connection was stronger.TakeawaysIn informal online learning environments, participants can form stable connections. The 30% threshold can be used to measure massive informal online learning networks in terms of commitment or persistence.

Computing the Kirchhoff index of a family of phenylene chain networks

Abstract The Kirchhoff index is a fundamental topological metric that provides insights into the structural and electrical characteristics of networks. It is defined as the sum of resistance distances between all pairs of nodes, serving as a key factor in understanding the dynamics within networks. To investigate the impact of structural variations on the Kirchhoff index, we select a family of phenylene chain networks as our model and establish a methodology to explore the Kirchhoff index using the Laplacian spectrum. By analyzing the network structure, we introduce a parameter to control the number of iterations, providing a recursive relationship between the Laplacian matrix and its eigenvalues at intervals of generations. This approach enables the derivation of an analytical expression for both the sum of the reciprocals of all nonzero Laplacian eigenvalues and the Kirchhoff index.

Sustainable optimisation of SMEs closed-loop supply chain for lithium battery manufacturing

Maintaining the sustainability of small and medium-sized enterprises (SMEs) supply chain has positive impact on society. Under the ripple effect, the vulnerability of the SMEs supply chain and the collaboration uncertainty of participants have potentially negative impact on the sustainability that cannot be ignored. We consider the closed-loop SMEs supply chain network design based on the risk propagation to improve the triple sustainability. The basis of the simulation model structure is a bi-level multi-objective mixed-integer linear programming model that aims to maximise triple sustainability, and an improved dynamic non-dominated genetic algorithm (DNSGA2) is designed to solve the problem. An SMEs supply chain for closed-loop manufacturing of lithium batteries in China was considered. The results show that the centralised SMEs supply chain preform better when the supply-demand imbalance, and the distributed network structure is preform better in maintaining sustainability when manufacturer capacity imbalances. Supply chain managers should develop more flexible strategies based on the structure of downstream demand, increasing upstream manufacturers' participation to enhance their viability and improve the sustainability of the supply chain.

Regular Dodecahedron-Based Network Structures

The packing and assembly of Platonic solids have fascinated mathematicians for ages. Recently, this fundamental geometrical problem has also attracted the attention of physicists, chemists, and engineers. This growing interest is due to the rapid advancements in various related fields, ranging from the formation of colloidal crystals and the design of metal–organic frameworks to the development of ultra-lightweight metamaterials, which are closely tied to the fast-evolving 3D printing technology. Numerous reports have focused on the assembly of Platonic polyhedra, particularly tetrahedra, for which an optimal packing strategy remains unidentified to this day. However, less attention has been given to the dodecahedron and its networks. This work introduces a new type of framework, designed from regular dodecahedra combined with icosahedron-based binders. The relatively simple design protocol employed here results in a remarkable variety of intriguing networks, which could be potentially useful in fields such as architecture, regenerative medicine, or aeronautics. Additionally, the dodecahedral networks presented in this study led to the discovery of intriguing structures resembling distorted graphene sheets. These structures exhibit features characteristic of both graphene and diamond.

FA-HRNet: A New Fusion Attention Approach for Vegetation Semantic Segmentation and Analysis

Semantic segmentation of vegetation in aerial remote sensing images is a critical aspect of vegetation mapping. Accurate vegetation segmentation effectively informs real-world production and construction activities. However, the presence of species heterogeneity, seasonal variations, and feature disparities within remote sensing images poses significant challenges for vision tasks. Traditional machine learning-based methods often struggle to capture deep-level features for the segmentation. This work proposes a novel deep learning network named FA-HRNet that leverages the fusion of attention mechanism and a multi-branch network structure for vegetation detection and segmentation. Quantitative analysis from multiple datasets reveals that our method outperforms existing approaches, with improvements in MIoU and PA by 2.17% and 4.85%, respectively, compared with the baseline network. Our approach exhibits significant advantages over the other methods regarding cross-region and cross-scale capabilities, providing a reliable vegetation coverage ratio for ecological analysis.

YOLO-APDM: Improved YOLOv8 for Road Target Detection in Infrared Images

A new algorithm called YOLO-APDM is proposed to address low quality and multi-scale target detection issues in infrared road scenes. The method reconstructs the neck section of the algorithm using the multi-scale attentional feature fusion idea. Based on this reconstruction, the P2 detection layer is established, which optimizes network structure, enhances multi-scale feature fusion performance, and expands the detection network’s capacity for multi-scale complicated targets. Replacing YOLOv8’s C2f module with C2f-DCNv3 increases the network’s ability to focus on the target region while lowering the amount of model parameters. The MSCA mechanism is added after the backbone’s SPPF module to improve the model’s detection performance by directing the network’s detection resources to the major road target detection zone. Experimental results show that on the FLIR_ADAS_v2 dataset retaining eight main categories, using YOLO-APDM compared to YOLOv8n, mAP@0.5 and mAP@0.5:0.95 increased by 6.6% and 5.0%, respectively. On the M3FD dataset, mAP@0.5 and mAP@0.5 increased by 8.1% and 5.9%, respectively. The number of model parameters and model size were reduced by 8.6% and 4.8%, respectively. The design requirements of the high-precision detection of infrared road targets were achieved while considering the requirements of model complexity control.

Survey on Wireless Sensor Network Structure Attacks and Energy Optimization

A Wireless Sensor Network (WSN) is a novel form of embedded real-time device that can be used for a variety of applications that make conventional networking impractical. Concerning the energy production of the nodes, WSN has significant issues that may affect the system's stability. Researchers have developed a range of methods and strategies to decrease wireless sensor network energy consumption. This paper has brief WSN structure for the optimization of communication with routing techniques. As network is vulnerable hence attacks were also present. This paper has list attacks as per number of layers. Further paper has list some of latest research work that not only optimize the network energy but also increases the robustness. Index Terms— Adhoc Network, Wireless Sensor Network, Communication Attacks, Virtual machines.

Analysis of spatial correlation network of urban digital economy in China

ABSTRACT In the context of the digital economy, the structure of China's urban spatial correlation network is undergoing profound changes. Based on social network analysis, this study analyzes the overall network characteristics, city network status and influencing factors of the spatial network of digital economy in Chinese cities. The study finds that China's digital economy presents a complex network of spatial connections, with increasing network strength, a clear ‘core-edge’ hierarchy, and a stable network structure. The developed cities in the east are the center of the network, the less developed cities in the east undertake the function of network radiation, and the cities in the west are located at the edge of the network. Technological innovation, industrial structure, human capital and urbanization all contribute to the spatial relevance of the digital economy. This study helps cities work together to amplify the digital dividend and narrow the digital divide.

Integrating fast iterative filtering and ensemble neural network structure with attention mechanism for carbon price forecasting

Accurate carbon price forecasts are crucial for policymakers and enterprises to understand the dynamics of carbon price fluctuations, enabling them to formulate informed policies and investment strategies. However, due to the non-linear and non-stationary nature of carbon price, traditional models often struggle to achieve high prediction accuracy. To address this challenge, this study proposes a novel integrated prediction framework designed to enhance forecast accuracy. First, the carbon price series is decomposed into a series of smoother subsequences using fast iterative filtering (FIF). Subsequently, an integrated prediction model, AM-TCN-LSTM, is constructed, incorporating the attention mechanism (AM), temporal convolutional networks (TCN), and long short-term memory (LSTM) neural networks. The attention mechanism adaptively captures complex features from multiple factors, while the TCN-LSTM efficiently extracts temporal features from the sequences. Finally, the results from each subsequence are aggregated to generate the final prediction. Five carbon markets in china: Guangdong, Hubei, Shenzhen, Beijing, and Shanghai were selected to verify the validity of the proposed model. Various comparative models and evaluation metrics were employed to assess performance. The results demonstrate that: (1) the TCN-LSTM model achieves higher prediction accuracy compared to single models. (2) FIF is a more effective decomposition method with superior performance compared to EMD-based methods. (3) The proposed model exhibits the highest predictive capability, with MAE values of 0.0964, 0.1403, 1.9476, 2.0848, and 0.5029 for the five carbon markets, significantly outperforming comparison models. (4) The attention mechanism effectively captures the influence of multiple factors on carbon price, particularly within the short-term components.

Residual trio feature network for efficient super-resolution

Deep learning-based approaches have demonstrated impressive performance in single-image super-resolution (SISR). Efficient super-resolution compromises the reconstructed image’s quality to have fewer parameters and Flops. Ensured efficiency in image reconstruction and improved reconstruction quality of the model are significant challenges. This paper proposes a trio branch module (TBM) based on structural reparameterization. TBM achieves equivalence transformation through structural reparameterization operations, which use a complex network structure in the training phase and convert it to a more lightweight structure in the inference, achieving efficient inference while maintaining accuracy. Based on the TBM, we further design a lightweight version of the enhanced spatial attention mini (ESA-mini) and the residual trio feature block (RTFB). Moreover, the multiple RTFBs are combined to construct the residual trio network (RTFN). Finally, we introduce a localized contrast loss for better applicability to the super-resolution task, which enhances the reconstruction quality of the super-resolution model. Experiments show that the RTFN framework proposed in this paper outperforms other state-of-the-art efficient super-resolution methods in terms of inference speed and reconstruction quality.

An integrated empirical and computational study to decipher help-seeking behaviors and vocal stigma

BackgroundProfessional voice users often experience stigma associated with voice disorders and are reluctant to seek medical help. This study deployed empirical and computational tools to (1) quantify the experience of vocal stigma and help-seeking behaviors in performers; and (2) predict their modulations with peer influences in social networks.MethodsExperience of vocal stigma and information-motivation-behavioral (IMB) skills were prospectively profiled using online surveys from a total of 403 Canadians (200 singers and actors and 203 controls). Data were used to formulate an agent-based network model of social interactions on vocal stigma (self-stigma and social-stigma) and help-seeking behaviors. Network analysis was performed to evaluate the effect of social network structure on the flow of IMB among virtual agents.ResultsLarger social networks are more likely to contribute to an increase in vocal stigma. For small social networks, total stigma is reduced with higher total IMB but not much so for large networks. For agents with high social-stigma and risk for voice disorder, their vocal stigma is resistant to large changes in IMB ( > 2 standard deviations). Agents with extreme IMB and stigma values are likely to polarize their networks faster in larger social groups.ConclusionsWe integrated empirical surveys and computational techniques to contextualize vocal stigma and IMB beyond theory and to quantify the interaction among stigma, health-seeking behavior and influence of social interactions. This work establishes an effective, predictable experimental platform to provide scientific evidence in developing interventions to reduce health stigma in voice disorders and other medical conditions.

Fuzzy clustering with Barber modularity regularization

In this paper, we propose a new algorithm for the joint clustering of two sets of statistical units N\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {N}$$\\end{document} and M\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {M}$$\\end{document} which are also equipped with an adjacency structure which is represented by a bipartite network. Our model is based on the fuzzy Partition Around Medoids, and it combines it with techniques for community detection in bipartite complex networks based on Barber modularity maximization. The goal is to produce a partition of N∪M\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {N}\\cup \\mathcal {M}$$\\end{document} into clusters, each of which is also identified by two medoids, one in N\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {N}$$\\end{document} and one in M\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {M}$$\\end{document}, which represent the typical units in the cluster for each set. Such clusters are optimized so that units in the same cluster both have similar values on their attributes and are likely to be adjacent. We test the algorithm on both simulated and real data, to show how it is able to capture a wide range of different interactions between the distribution of the attributes and the network structure.

Constructing representative group networks from tractography: lessons from a dynamical approach

Human group connectome analysis relies on combining individual connectome data to construct a single representative network which can be used to describe brain organisation and identify differences between subject groups. Existing methods adopt different strategies to select the network structural features to be retained or optimised at group level. In the absence of ground truth, however, it is unclear which structural features are the most suitable and how to evaluate the consequences on the group network of applying any given strategy. In this investigation, we consider the impact of defining a connectome as representative if it can recapitulate not just the structure of the individual networks in the cohort tested but also their dynamical behaviour, which we measured using a model of coupled oscillators. We applied the widely used approach of consensus thresholding to a dataset of individual structural connectomes from a healthy adult cohort to construct group networks for a range of thresholds and then identified the most dynamically representative group connectome as that having the least deviation from the individual connectomes given a dynamical measure of the system. We found that our dynamically representative network recaptured aspects of structure for which it did not specifically optimise, with no significant difference to other group connectomes constructed via methods which did optimise for those metrics. Additionally, these other group connectomes were either as dynamically representative as our chosen network or less so. While we suggest that dynamics should be at least one of the criteria for representativeness, given that the brain has evolved under the pressure of carrying out specific functions, our results suggest that the question persists as to which of these criteria are valid and testable.

Social networks from dailies: the observer’s point of view

In this paper, we explore the hypothesis that the social network described by an observer in a written document provides a discernible cross-section of the underlying social network. Specifically, we propose that the subnetwork described by the observer is extracted from large social network with a Barabási-Albert structure, following two key premises: (1) the observer identifies and reports nodes and interactions in his local environment, and (2) the observer records the most relevant nodes—those with a high number of connections—as well as the most significant portions of their local environment. We test this hypothesis using personal dailies written in New Spain and Mexico between 1776 and 1873. We compare the structure of these dailies networks to one depicted by a typical subnetwork of a Barabási-Albert network, obtained following the two premises mentioned above. To support our findings, we compare them to the social networks described in two well-known novels. In this work, we implicitly assume the hypothesis that there is a complete, non-subjective social network, including our observer, with a Barabási-Albert structure. The evolution of this underlying social network can shed light on the dynamics of a society over time. Our approach could give place to indicators of the changes in the structure of this enveloping network. This is especially important since, although our historical sources do not offer sufficient data to reconstruct the processes in their entirety, the approach could supply clues or fragments from which we could understand the structure and dynamics of societies over time.