Network Tools Research Articles

SG-ATT: A Sequence Graph Cross-Attention Representation Architecture for Molecular Property Prediction.

Existing formats based on the simplified molecular input line entry system (SMILES) encoding and molecular graph structure are designed to encode the complete semantic and structural information of molecules. However, the physicochemical properties of molecules are complex, and a single encoding of molecular features from SMILES sequences or molecular graph structures cannot adequately represent molecular information. Aiming to address this problem, this study proposes a sequence graph cross-attention (SG-ATT) representation architecture for a molecular property prediction model to efficiently use domain knowledge to enhance molecular graph feature encoding and combine the features of molecular SMILES sequences. The SG-ATT fuses the two-dimensional molecular features so that the current model input molecular information contains molecular structure information and semantic information. The SG-ATT was tested on nine molecular property prediction tasks. Among them, the biggest SG-ATT model performance improvement was 4.5% on the BACE dataset, and the average model performance improvement was 1.83% on the full dataset. Additionally, specific model interpretability studies were conducted to showcase the performance of the SG-ATT model on different datasets. In-depth analysis was provided through case studies of in vitro validation. Finally, network tools for molecular property prediction were developed for the use of researchers.

Online shopping consumer perception analysis and future network security service technology using logistic regression model.

In order to understand consumer perception, reduce risks in online shopping, and maintain online security, this study employs data envelopment analysis (DEA) to confirm the relationship between evaluation and stimuli. It establishes a model of stimuli-organism response and uses regression analysis to explore the relationships among negative online shopping evaluations, consumer perception of risk, and consumer behavior. This study employs attribution theory to analyze the impact of evaluations on consumer behavior and assesses the role of perceived risk as a mediator. The independent variable is negative comments, the dependent variable is consumer behavior, and logistic regression is used to empirically analyze the factors influencing online shopping security. The results indicate a positive correlation between the number of negative comments and consumers' delayed purchase behavior, with a correlation coefficient of 41%. The intensity of negative comments significantly impacts consumers' refusal to make a purchase, with a correlation coefficient of 38%. The length of negative comments substantially influences consumers' opposition to purchasing, also with a correlation coefficient of 38%. There is a close relationship between perceived risk and consumers' delayed shopping behavior and the number of negative comments, with 41% and 4% correlation coefficients, respectively. Perceived risk has a relatively smaller impact on consumers' opposition to purchase behavior, with a correlation coefficient of 27%. The length, intensity, and number of negative comments are correlated with consumers' opposition, refusal, and delayed consumption, negatively affecting consumer intent. Additionally, negative comments are related to perceived risk and consumer behavior. Perceived risk causally influences consumer behavior, while the convenience of shopping has a relatively minor impact on online shopping security. Factors like delivery speed, buyer reviews, brand, price, and consumer perception are significantly related to online shopping security. Consumer perception has the most significant impact on online shopping security, balancing secure and fast consumption under the guarantee of user experience. Strengthening consumer perception enhances consumers' ability to process risk information, helping them better identify risks and avoid using hazardous network software, tools, or technologies, thereby reducing potential online security risks.

Machine learning, conventional and statistical physics modeling of 2,4-Dichlorophenoxyacetic acid (2,4-D) herbicide removal using biochar prepared from Vateria indica fruit biomass

The adsorption properties of 2,4-Dichlorophenoxyacetic acid (2,4-D) onto biochar, obtained through HCl-assisted hydrothermal carbonization process of Vateria indica fruits (VI-BC), were extensively studied using traditional and statistical physics approaches. The traditional adsorption investigations encompassed kinetics, equilibrium, and thermodynamics studies. Subsequently, the Hill statistical physics model was employed to interpret the mechanism. Also, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) machine learning tools were successfully employed to model the adsorption data wherein both models had high prediction potential (R2 > 0.99). The outcomes demonstrated that the produced VI-BC exhibited remarkable adsorptive traits, having a considerable specific surface area (111.54 m2/g), pore size (5.89 nm), a variety of functional groups, and appropriate attributes for efficiently adsorbing 2,4-D. For 10 mg/L 2,4-D, at pH 2.0 and with 0.3 g/L dose, an impressive 91.67% adsorption efficiency was achieved within a 120-min. Pseudo-second-order model aptly depicted the kinetic behavior of 2,4-D adsorption, while the Freundlich model provided a more accurate representation of the isotherms. 2,4-D maximum adsorption capacity stood at 131.39 mg/g at 303 K. The Hill statistical physics model elucidated that the adsorption primarily occurred via physisorption mechanisms, involving electrostatic attractions, π-π conjugation, and pore filling. This conclusion was further substantiated by post-adsorption characterization of the VI-BC. Thermodynamic analysis indicated that the interactions between VI-BC and 2,4-D were favorable, spontaneous, and exothermic. The calculated low energy of adsorption (1.255 kJ/mol) and ΔH° value (−20.49 kJ/mol) further supported physisorption as the dominant mechanism. In summary, this study underscores the significant potential of the newly developed biochar as a promising alternative material for efficiently removing the 2,4-D herbicide from polluted environments.

Estimating Functional Brain Networks by Low-Rank Representation With Local Constraint.

The functional architecture undergoes alterations during the preclinical phase of Alzheimer's disease. Consequently, the primary research focus has shifted towards identifying Alzheimer's disease and its early stages by constructing a functional connectivity network based on resting-state fMRI data. Recent investigations show that as Alzheimer's Disease (AD) progresses, modular tissue and connections in the core brain areas of AD patients diminish. Sparse learning methods are powerful tools for understanding Functional Brain Networks (FBNs) with Regions of Interest (ROIs) and a connectivity matrix measuring functional coherence between them. However, these tools often focus exclusively on functional connectivity measures, neglecting the brain network's modularity. Modularity orchestrates dynamic activities within the FBN to execute intricate cognitive tasks. To provide a comprehensive delineation of the FBN, we propose a local similarity-constrained low-rank sparse representation (LSLRSR) method that encodes modularity information under a manifold-regularized network learning framework and further formulate it as a low-rank sparse graph learning problem, which can be solved by an efficient optimization algorithm. Specifically, for each modularity structure, the Schatten p-norm regularizer reduces the reconstruction error and provides a better approximation of the low-rank constraint. Furthermore, we adopt a manifold-regularized local similarity prior to infer the intricate relationship between subnetwork similarity and modularity, guiding the modeling of FBN. Additionally, the proximal average method approximates the joint solution's proximal map, and the resulting nonconvex optimization problems are solved using the alternating direction multiplier method (ADMM). Compared to state-of-the-art methods for constructing FBNs, our algorithm generates a more modular FBN. This lays the groundwork for further research into alterations in brain network modularity resulting from diseases.

A pyridinium-based strategy for lysine-selective protein modification and chemoproteomic profiling in live cells.

Protein active states are dynamically regulated by various modifications; thus, endogenous protein modification is an important tool for understanding protein functions and networks in complicated biological systems. Here we developed a new pyridinium-based approach to label lysine residues under physiological conditions that is low-toxicity, efficient, and lysine-selective. Furthermore, we performed a large-scale analysis of the ∼70% lysine-selective proteome in MCF-7 cells using activity-based protein profiling (ABPP). We quantifically assessed 1216 lysine-labeled peptides in cell lysates and identified 386 modified lysine sites including 43 mitochondrial-localized proteins in live MCF-7 cells. Labeled proteins significantly preferred the mitochondria. This pyridinium-based methodology demonstrates the importance of analyzing endogenous proteins under native conditions and provides a robust chemical strategy utilizing either lysine-selective protein labeling or spatiotemporal profiling in a living system.

Transitions from Long-Term Unemployment to Employment

As labour market research has shown, social networks heavily impact the likelihood of gaining employment. In Germany, factors such as low qualifications, illness, age over 50, immigrant background, language deficits, and care obligations, particularly when combined, hinder job transitions and induce long-term unemployment. Nevertheless, some long-term unemployed individuals can secure jobs and forgo social benefits, seemingly against all odds. To investigate the transition-to-work process and its relationship with information, network compositions, and individual agency, we conducted a qualitative survey supplemented by problem-centred biographical interviews, employing software-based network tools. The results revealed that adopting a process-oriented approach was crucial for understanding the utilization of networks to address long-term unemployment. To encourage future network approaches in investigating labour market transitions, three key aspects appear valuable. First, the development of individual agency within stable living conditions, the contribution of alteri within the network, and information transfer for labour market orientation, including factors contributing to translating information, motivation, and orientation into employment activities. This three-step analysis moves beyond traditional approaches examining factors such as network density and the frequency of interactions or simply distinguishing strong from weak ties, by emphasising relational diachronic perspectives and respecting individual life course trajectories.

Impact of Social Networks on Learning Activities

Chad learning system is one of the least modernized in central Africa. But with the social network tools proliferation, we see that students use these tools. This work aims to see if students really use social networks in their learning activities and how they appreciate them. Also, we wonder if social networks are well used by students and what are their repercussions on students' learning activities. A survey of students from public and private higher education establishments in the city of N’Djamena, capital of the Republic of Chad, was carried out using a questionnaire from 13 March to 18 April 2023. 202 students have taken part. The age of the population studied is between 16 and 30 years old. The results show that almost students (99,5% of those interviewed) is aware of social networks and 86% use them with high frequency among those aged 20 to 25 (89.3%). 93.6% of learners find that social networks help them in their studies but 31.7% find that social networks disrupt them in their learning activities (courses and revisions). Like all users, learners are exposed to social divisions, scams, harassment and other harms of social networks. This study gives a comprehensive understanding of the impact of social network on students learning activities in Chad. It can help decision-makers to reconsider educational policy in order to integrate social media tool to promote not only the success of learners but also to ensure living cohesion which could be undermined through social networks.

An overview of environmental risk factors for type 2 diabetes research using network science tools.

Current studies lack a comprehensive understanding of the environmental factors influencing type 2 diabetes, hindering an in-depth grasp of the overall etiology. To address this gap, we utilized network science tools to highlight research trends, knowledge structures, and intricate relationships among factors, offering a new perspective for a profound understanding of the etiology. The Web of Science database was employed to retrieve documents relevant to environmental risk factors in type 2 diabetes from 2012 to 2024. Bibliometric analysis using Microsoft Excel and OriginPro provided a detailed scientific production profile, including articles, journals, countries, and authors. Co-occurrence analysis was employed to determine the collaboration state and knowledge structures, utilizing social network tools such as Gephi, Tableau, and R Studio. Additionally, theme evolutionary analysis was conducted using SciMAT to offer insights into research trends. The publications and themes related to environmental factors in type 2 diabetes have consistently risen, shaping a well-established research domain. Lifestyle environmental factors, particularly diet and nutrition, stand out as the most represented and rapidly growing topics. Key focal hotspots include sedentary and digital behavior, PM2.5, ethnicity and socioeconomic status, traffic and greenspace, and depression. The theme evolutionary analysis revealed three distinct paths: (1) oxidative stress-air pollutants-PM2.5-air pollutants; (2) calcium-metabolic syndrome-cardiovascular disease; and (3) polychlorinated biphenyls (PCBs)-persistent organic pollutants (POPs)-obesity. Digital behavior signifies a novel approach for preventing and managing type 2 diabetes. The influence of PM2.5 and calcium on oxidative stress and abnormal vascular contraction is intricately linked to microvascular diabetes complications. The transition from PCBs and POPs to obesity underscores the disruption of endocrine function by chemicals, elevating the risk of diabetes. Future studies should explore the connections between environmental factors, microvascular complications, and long-term outcomes in diabetes.

Nutrient sequestration and lipid production potential of Chlorella vulgaris under pharmaceutical wastewater treatment: experimental, optimization, and prediction modeling studies.

The efficient management and treatment of pharmaceutical industry wastewater (PIWW) have become a serious environmental issue due to its high toxicity. To overcome this problem, the present study deals with the phycoremediation of PIWW using Chlorella vulgaris microalga isolated from the Ganga River at Haridwar, India. For this, response surface methodology (RSM) and artificial neural network (ANN) tools were used to identify the best reduction of total phosphorus (TP) and total Kjeldahl's nitrogen (TKN) based pollutants along with the lipid production efficiency of C. vulgaris. Three different concentrations of pharmaceutical wastewater (0, 50, and 100%), operating temperatures (20, 25, and 30 °C), and light intensity (2000, 3000, and 4000 lx) were used to design the phycoremediation experiments having 6:18 h of dark/light period and reactor functional volume of 15L. Findings revealed that C. vulgaris was good enough to remove maximum TP (90.35%), TKN (83.55%) along 20.88% of lipid yield at 25.62 °C temperature, 60.73% PIWW concentration, and 4000 lx of light intensity, respectively. Based on the model performance and validation results, ANN showed more accuracy as compared to the RSM tool. Therefore, the findings of this study showed that C. vulgaris is capable of treating PIWW efficiently along with significant production of lipid content which can further be used in various applications including biofuel production.

Epilepsy Networks and Their Surgical Relevance.

Surgical epilepsy is a rapidly evolved field. As the understanding and concepts of epilepsy shift towards a network disorder, surgical outcomes may shed light on numerous components of these systems. This review documents the evolution of the understanding of epilepsy networks and examines the data generated by resective, ablative, neuromodulation, and invasive monitoring surgeries in epilepsy patients. As these network tools are better integrated into epilepsy practice, they may eventually inform surgical decisions and improve clinical outcomes.

Food system resilience: Unraveling power relations and the Matthew effect in farmers networks

Farmer Producer Companies (FPCs) are new-generation food systems meant to address development concerns in rural India. While FPCs were established on cooperative ideals, legacy power relations often persist, with certain actors exerting decisive control over market connections and resources. As FPCs emerge from their pre-existing socio-economic conditions, their resilience is predicated on maintaining robust value chain relationships. In this study, we applied a multi-layered approach with network tools on field data to investigate the Matthew effect, where cumulative advantages to elites maintain or amplify inequality. We focused on network power discrepancies and democratic participation in two selected natural farming FPCs. Social network analysis (SNA) and two other visualization approaches, Self-organizing Maps and Parallel Categories Plots, have been deployed to analyse the member networks and FPC ecosystems, before and during the CoVID-19 pandemic. The FPC exhibiting a strong Matthew effect, with a legacy of preferential relationships, showed lower resilience under uncertainty compared to the other FPC, whose legacy of grassroots efforts involving marginal and women farmers managed to avert the Matthew effect. The findings offer valuable insights for the local governance of inclusive food systems and have global relevance, especially for addressing SDGs 5 (gender equality) and 10 (reduced inequalities).

A simplicial epidemic model for COVID-19 spread analysis.

Networks allow us to describe a wide range of interaction phenomena that occur in complex systems arising in such diverse fields of knowledge as neuroscience, engineering, ecology, finance, and social sciences. Until very recently, the primary focus of network models and tools has been on describing the pairwise relationships between system entities. However, increasingly more studies indicate that polyadic or higher-order group relationships among multiple network entities may be the key toward better understanding of the intrinsic mechanisms behind the functionality of complex systems. Such group interactions can be, in turn, described in a holistic manner by simplicial complexes of graphs. Inspired by these recently emerging results on the utility of the simplicial geometry of complex networks for contagion propagation and armed with a large-scale synthetic social contact network (also known as a digital twin) of the population in the U.S. state of Virginia, in this paper, we aim to glean insights into the role of higher-order social interactions and the associated varying social group determinants on COVID-19 propagation and mitigation measures.

Neural networks as a tool for improving the mathematical model of ship motion

Using neural networks opens up great opportunities for studying mathematical models of ship motion. Correction by a network of identified parameters of the selected model should be as adequate as possible to the results of standard full-scale tests defined by the IMO Resolution N 137 of 2002. A mathematical model in displacements is considered, containing 16 parameters that determine the hydrodynamic forces acting on the ship's hull and steering gear, and is the source of a data set for training the network by randomly varying the parameters and subsequent computer testing. The standard maneuver is a steady-state circulation with fixation of the maneuvering elements: diameter, linear velocity, drift angle and angular velocity of rotation. Improving the quality of the model has consisted of changing its parameters and minimizing the mean square errors of the values of the maneuvering elements obtained during testing. For these purposes, a neural network with 16 inputs (model parameters) and four outputs (maneuvering elements for steady-state circulation) has been built. The data set for training the network was obtained using a program developed by the authors and intended for calculating parameters and conducting maneuver tests. A tanker with a displacement of 30,000 tons was chosen as a test object. Various options for network architecture and tools for working with it have been considered; the Statistica Neural Nets (SNN) software environment and the ANN package in the SciLab environment have been used. Comparative assessments of the results of working with these tools have been given.

CURRENT STATE AND PROSPECTS OF INVESTMENT ATTRACTION INTO THE AQUACULTURE SECTOR IN UKRAINE

Topicality. Preliminary calculations confirm the significant potential for the development of aquaculture both in the Ukrainian Black Sea region and in Ukraine as a whole. The increase of aquaculture share in the total fish consumption in Ukraine in the conditions of the war, which caused a significant reduction in commercial fishing due to the physical loss of the water areas of the Azov and Black Seas, the consequences of technogenic pollution of water bodies due to the explosion of the Kakhovskaya Hydroelectric Power Station, is also a confirmation of the importance of aquaculture for the food security of the country and the region.
 Aim and tasks. The presented study aims to analyze the current state of investment attraction in the aquaculture sector in Ukraine and the world, to identify obstacles and prospects for it increasing.
 Materials and Methods. In the research the methods of analysis and synthesis, theoretical generalization, statistical analysis, retrospective analysis, system analysis have been used, on the basis of which the cause-and-effect relationships of low investment activity in the aquaculture sector in Ukraine are determined, proposals are systematized regarding the organizational and economic mechanisms of attracting investments to the aquaculture sector according to the resource-process approach - stages of value creation (preparation, production, processing, realiztion) and input components (factors of production) necessary for the functioning of the sector.
 Research results. The analysis of investments in the aquaculture sector in Ukraine and the Ukrainian Black Sea region revealed its extremely unsatisfactory state, which does not correspond to the interests of the sector's development: a small volume of capital investments in the aquaculture sector - 0.038% of the total volume of capital investments of enterprises in Ukraine; unsatisfactory investment support for innovative activities of aquaculture enterprises - only 0.5% of the total volume of capital investments is directed to intangible assets; the sources of capital investments are mainly own funds of enterprises, funds from local budgets, investment companies and funds, funds from non-resident investors and other sources are not involved at all; a small amount of foreign direct investment - 0.04% of the total amount of direct investment in Ukraine, there is an outflow of investments, FDI in the aquaculture sector in the regions of the Ukrainian Black Sea region has not been carried out in recent years; non-profitability of invested capital.
 Conclusion. In order to form a long-term vision of the development of the sector and determine the prospects for potential investors, it is necessary to develop an industry strategy and programs, in particular, regarding the attraction of investments in the aquaculture sector and the development of innovations. Targeted actions require the formation of an ecosystem of accompanying services for investors, in particular, centers for attracting investors, investment consulting agencies specializing in aquaculture, which is especially relevant for seaside regions. It is proposed to apply a comprehensive approach to the development of the aquaculture sector according to the value creation chain "preparation-production-processing-realization", that means the formation of a regional innovation-oriented complex of aquaculture production. The use of network tools for creating added value in the sector, such as clusters, water technology parks, eco-industrial parks, will create attractive objects for investment, in particular from abroad, taking into account their orientation to the principles of innovation, zero waste, environmental safety, resource conservation.

Application of computer vision to identify reference points for assessing postural disorders

The use of computer vision to determine reference points on the human back is a relatively new approach in medicine. The relevance of this study is due not only to the need to develop new methods and approaches in determining postural disorders, but also to the growing interest in the development and application of artificial intelligence in medicine. This study is devoted to the creation and training of a neural network to find reference points of the back when diagnosing postural disorders. In this study, we use a set of ready-made neural network tools that are freely available. Training was carried out on models of photographed volunteers to determine the reference points of the back. The results of the study indicate that, as part of the study, a search was made for alternative (reference points) that determine the geometry of the back using developed neural network algorithms. It was shown that the accuracy of point detection by a neural network trained on more than 2400 grouped photographs reaches 85%, which indicates a good determination of object boundaries and their classification. The use of computer vision to identify reference points for assessing postural abnormalities can bring significant benefits to medical practice. This tool may be more accurate and efficient than traditional diagnostic methods, as well as more accessible and convenient for the patient. In addition, the use of neural networks can speed up the diagnostic process and reduce research costs.

Using latent variable analysis to capture individual differences in bilingual language experience

Abstract Bilingualism is an experience that varies across a continuum and can change across the lifespan. Psychometric research is an underexplored avenue with the potential to further our understanding of the mechanisms and traits underlying bilingual experiences. Here, we developed and validated a social network questionnaire to measure sociolinguistic features in 212 individuals via personal social network. Confirmatory factor analysis examined the measurement structure of the variables. Compared to a one-factor model, the best fitting model was a two-factor model in which the language experience of the individual (i.e., ego) and the language experience of the individual's network (i.e., alters) were correlated latent factors under which aspects of the bilingual experience loaded. Additional analyses revealed other potential ways to examine the data in future analyses. These results provide the first measurement model of bilingual experiences, and provide support for theoretical accounts suggesting differential neuropsychological outcomes based on individual bilingual variability. The results also support the use of social network tools to capture differences in bilingualism.

Cuidados de enfermagem em saúde mental para pessoas com diabetes mellitus: revisão integrativa

Objective: evaluate the evidence available on mental health nursing care for people with diabetes mellitus at different levels of health care. Method: integrative literature review. The search was conducted in five databases. The sample consisted of 14 studies. The studies were exported to the EndNote manager and their data to a Microsoft Excel spreadsheet. The methodological quality of the studies was evaluated using tools proposed by the Joanna Briggs Institute. Sampling, categorization, evaluation, interpretation of the results, and synthesis of the included studies were carried out by two reviewers independently. The descriptive analysis of the results is presented in three categories. Results: self-care guidelines enhanced by the social support network, encompassing physical and psychological tools and strategies; therapeutic communication and psychotherapy strategies, focusing on psychotherapy and therapeutic communication; and self-management interventions, addressing self-care based on behavioral theories. Conclusion: the synthesis of knowledge revealed that guidelines for self-care enhanced by the social support network, psychotherapy and therapeutic communication strategies, and self-management interventions are positive interventions that contribute to people with mental disorders and diabetes mellitus in the prevention of diseases.

A Self-Organizing feature Map neural network for compton suppression in phoswich detector

Scintillation phoswich detectors can be used for particle discrimination and Compton suppression, and their function is based on pulse shape discrimination (PSD) methods. The conventional PSD methods mainly including Rise Time Discrimination (RTD), Rise and Decay time discrimination (R&D), Constant Time Discrimination (CTD), etc., but linear PSD methods have their limitations in processing pulse signals. Neural network tools can achieve nonlinear output by adjusting the weights between neurons during the training, which are suitable for identifying nonlinear signals generated by detectors. The self-organizing feature mapping (SOM) is the most commonly used clustering neural network, which can cluster signals based on their similarity through competitive training of neural networks without prior data. In this study, 3 SOM networks with different size are trained, and after comparison with the single LaBr3:Ce detector, the total accuracy of the phoswich detector using the SOM neural network for discrimination reaches over 99.5%, which is a 7.59% increase in accuracy compared to the RTD method, and the peak-to-total ratio (P/T) is improved by 1.07%.

APEX: an Annotation Propagation Workflow through Multiple Experimental Networks to Improve the Annotation of New Metabolite Classes in Caenorhabditis elegans.

Spectral similarity networks, also known as molecular networks, are crucial in non-targeted metabolomics to aid identification of unknowns aiming to establish a potential structural relation between different metabolite features. However, too extensive differences in compound structures can lead to separate clusters, complicating annotation. To address this challenge, we developed an automated Annotation Propagation through multiple EXperimental Networks (APEX) workflow, which integrates spectral similarity networks with mass difference networks and homologous series. The incorporation of multiple network tools improved annotation quality, as evidenced by high matching rates of the molecular formula derived by SIRIUS. The selection of manual annotations as the Seed Nodes Set (SNS) significantly influenced APEX annotations, with a higher number of seed nodes enhancing the annotation process. We applied APEX to different Caenorhabditis elegans metabolomics data sets as a proof-of-principle for the effective and comprehensive annotation of glycerophospho N-acyl ethanolamides (GPNAEs) and their glyco-variants. Furthermore, we demonstrated the workflow's applicability to two other, well-described metabolite classes in C. elegans, specifically ascarosides and modular glycosides (MOGLs), using an additional publicly available data set. In summary, the APEX workflow presents a powerful approach for metabolite annotation and identification by leveraging multiple experimental networks. By refining the SNS selection and integrating diverse networks, APEX holds promise for comprehensive annotation in metabolomics research, enabling a deeper understanding of the metabolome.

Information Technologies in Modern PR Activities

The purpose of the article is to highlight the importance of introducing information technologies into PR activities as a necessary element of the digital transformation of modern society, to characterize the most appropriate and effective means of information and communication technologies in the field of public relations and the effectiveness of their use by considering the PR activities of the American company Airbnb; to find out the main advantages of using interactive network technologies in the organization of public relations and to outline the challenges in implementing modern technologies. The research methods include analysis of previous studies on the use of information technology in PR activities, systematisation and generalisation to determine the effectiveness of information technology implementation in modern PR activities, content analysis of pages and official communication channels of Airbnb, including the corporate website, mobile application and social media. The scientific novelty lies in the reflection of current trends in the use of modern technologies in the field of public relations and the substantiation of the advantages and challenges of their implementation. For the sake of argumentation, the study analyses the digital PR communication of Airbnb in order to identify the key aspects of a successful strategy for using information technology in various areas of PR activities. Conclusions. The essence of the use of information technology in PR activities in the application of modern Internet communication channels, network tools and programs have been determined. It is confirmed that their implementation is a necessary aspect of digital transformation in modern society. The authors identify aspects of the effectiveness of information technology use: reputation control, crisis management, achievement of strategic business goals, and solving marketing tasks. Among the wide range of information tools, social networks, chatbots and immersive technologies are singled out, and their impact is substantiated. The main advantages of using information technologies are characterised: by expanding global access, flexibility and increasing the efficiency of measuring results; and challenges: lack of technical training, competition and information overload, risk of cyber-attacks and privacy violations. The study of Airbnb’s experience confirms the effectiveness of attracting the target audience through a mobile application, a user-friendly Internet platform, and a video campaign on YouTube.