Network Users Research Articles

Friend2User : A new CNN based method for user network and content embedding

Nowadays, social networks have become an integral part of modern society, significantly influencing individuals worldwide due to their extensive reach. Consequently, analyzing the data disseminated within these networks in order to identify online communities presents a major challenge for researchers in the data mining field. To address this challenge, we propose, in this paper, a novel deep user embedding framework for community extraction on social networks. Our method leverages the capability of Convolutional Neural Networks (CNNs) to produce abstract representations of users that preserve the semantic information in the data. Specifically, our approach considers both the profile content and the network structure, harnessing the power of unsupervised CNNs. The key concept underlying our proposal is that each user is represented not only by their own content but also by the content of their close friends. We employ a recursive CNN to integrate neighboring users’ content, thereby generating concise and informative user embeddings. The empirical findings obtained by our method demonstrate the effectiveness of our proposed user embeddings in efficiently detecting communities within social networks, particularly in the context of cybersecurity.

IOS-based mobile application for network performance analysis

In today's world, when technology is developing faster and faster, many people face network malfunctions and threats. Ensuring the security and performance of your own network is a critical task for many IT professionals and ordinary users. There are many tools and ways to monitor, analyse and secure your network, but a mobile app is an extremely convenient option. A mobile application that combines several network functions at once saves time and is always at hand. Networking tools in a smartphone can replace many different desktop applications and websites (web APIs), provide command line tools, etc. The iOS operating system has high security standards and its ecosystem is quite closed. This protects users from a lot of malware and makes the device extremely resistant to hacking. However, it also imposes some restrictions on the network and system for the average user. That's why iOS apps that include a variety of networking tools are a really useful tool for users. To keep the network in an efficient and secure state, iOS has specific ways to monitor and detect network problems: Wi-Fi analysers, checking network routes and interfaces, checking Internet speed, ping, traceroute, etc. The use of these technologies in the application under consideration will enable efficient analysis of network productivity.

GNNRI: detecting anomalous social network users through heterogeneous information networks and user relevance exploration

GNNRI: detecting anomalous social network users through heterogeneous information networks and user relevance exploration

Междометия как средства выражения эмпатии в русскоязычной сетевой коммуникации

<p>The work is devoted to the peculiarities of the use of interjections by Internet communicants as a means of expressing empathy in comments-reactions to posts on the social network TikTok. It has been established that discursive practices of compassion involve a significantly larger number of users than practices of co-rejoicing, and, accordingly, demonstrate a wider range of interjection reactions. The author analyzed 60 Russian-language network polylogues (threads), the unifying discourse-forming feature of which is the collective intentional state of rejoicing. Based on linguopragmatic and discursive approaches, the relationship between the realized intentional-illocutionary meanings of interjections and their regulatory-dialogue potential in communication mediated by a network format is considered. When creating emotiogenic content, the author of a video post can offer the audience interjection stimuli, presented in the form of captions on the message or in the form of hashtags. The goal of the initiator of network communication is to gain approval and support from other network users and influence their emotional state. The analysis showed that in network “oral-written” communication, other participants in the communication react interactively to interjections used by some users; as a result, not only repetitions are traced in the virtual polylogue, but also entire chains of the same or similar interjections in semantics and pragmatics units. The repertoire and ways of using interjections as a means of expressing the emotions of online interlocutors demonstrate their conditioning by the media culture of TikTok as a special digital platform. Specific phenomena of such media culture include users’ ideas about acceptable norms of emotional manifestations and cognitive scenarios of emotions that are consistently embodied in communication. Observations of the use of interjections have shown that communication on social networks can have a quasi- or pseudo-empathic meaning.</p>

The Effect of Social Media Influencer on Brand Switching Behavior of Millennial and Generation Z

This study examines the effect of social media influencers on brand switching behaviors among Millennials and Generation Z in urban areas of Nepal, specifically focusing on the major cities of Kathmandu and Lalitpur. A sample size of 150 respondents was determined using the rule of 1:10. Data were analyzed using regression analysis to assess the relationships between social media influencers and brand switching. The findings indicate that social media influencers play a significant role in influencing customer decisions regarding brand switching. In contrast, opinion leaders and celebrities did not show any significant influence on this behavior. This suggests that social network users, particularly younger consumers, have a substantial impact on brand switching behaviors. The study also highlights the prevalent use of social media among younger demographics, particularly on platforms like Instagram, reinforcing the importance of social media in shaping consumer choices and opinions. The results have important implications for businesses operating in the digital age. By effectively leveraging social media influencers, companies can better tap into the preferences and motivations of Millennials and Generation Z, ultimately fostering brand loyalty and trust. Overall, this research contributes to the understanding of the dynamics between social media influencers and consumer behavior, emphasizing the need for businesses to adapt their marketing strategies to engage effectively with younger consumers in an increasingly digital marketplace.

Priority-based band sharing for secondary users in cognitive radio networks

To improve wireless spectrum utilization, cognitive radio networks (CRNs) allow secondary users (SUs) opportunistic access to the frequency bands owned by primary users (PUs), when such PUs are inactive. A fully distributed CRN architecture is quite favorable, as it does not require any centralized scheduling, nor does it require coordination between SUs via a common control channel. This can reduce cost and avoid single point-of-failure issues. However, uncoordinated access to the spectrum by competing SUs, if not done judiciously, can cause excessive collisions and limit performance, which can be detrimental to the usefulness of CRNs, especially if some SUs have urgent traffic to send. Hence, we present a novel protocol to support traffic with multiple classes of priority within a fully distributed CRN. In such protocol, competing SUs attempt to access the spectrum autonomously, but rationally, based on their priority level, opting to transmit their packets when it is beneficial to both the SU itself and also to the overall system performance. Simulations confirm that the proposed protocol achieves excellent overall network performance, while ensuring that higher priority packets experience higher throughput and smaller delay compared to lower priority packets.

A user-centric model of connectivity in street networks

Modelling street network connectivity is a fundamental research problem in transportation science. Here, we argue that the connectivity of a street network cannot be defined independently from the users of that street network. That is, different users will experience different levels of connectivity depending on their corresponding travel behaviour. In this work, we propose a model of connectivity that is defined with respect to a user’s travel behaviour within a given street network. We demonstrate that many real-world problems can be posed as instances of an optimisation problem defined with respect to this model. This includes optimising a user’s home location with respect to their connectivity and optimising the location of a facility with respect to the connectivity of its users. We prove the above optimisation problem is NP-hard and present an integer programming solution that scales to large problem instances.

Anonymous group structure algorithm based on community structure.

A social network is a platform that users can share data through the internet. With the ever-increasing intertwining of social networks and daily existence, the accumulation of personal privacy information is steadily mounting. However, the exposure of such data could lead to disastrous consequences. To mitigate this problem, an anonymous group structure algorithm based on community structure is proposed in this article. At first, a privacy protection scheme model is designed, which can be adjusted dynamically according to the network size and user demand. Secondly, based on the community characteristics, the concept of fuzzy subordinate degree is introduced, then three kinds of community structure mining algorithms are designed: the fuzzy subordinate degree-based algorithm, the improved Kernighan-Lin algorithm, and the enhanced label propagation algorithm. At last, according to the level of privacy, different anonymous graph construction algorithms based on community structure are designed. Furthermore, the simulation experiments show that the three methods of community division can divide the network community effectively. They can be utilized at different privacy levels. In addition, the scheme can satisfy the privacy requirement with minor changes.

Forensic Significance of Certain Types of Computer Networks

Stating the great forensic potential of computer networks, the author differentiates computer networks into 6 types (in terms of functionality and level of service): personal (BAN), personal (PAN), local (LAN), campus (CAN), urban (MAN) and global networks (WAN). It seems that the BAN and PAN level networks can be conditionally combined into a single group — personal networks (some of its devices can be part of both one and the other network or be elements of both at the same time). It is logical to accumulate LAN and CAN networks into a group — collective networks, and megapolis (MAN) and global (WAN) networks into a group — multiuser networks. In the paper, the author focuses on collective and multiuser networks. At the same time, it is also indicated that there is a need to allocate a national level of computer networks. The author examines the criminalistically significant fundamentals of the functioning of the networks under consideration. The paper determines the specifics of the work and the main tasks of the researcher in the study of networks of various levels. It is stated that relevant information can be extracted from almost any type of network. Awareness of the type, hierarchy and level of network coverage will help optimize the investigation by increasing the speed of identifying the provider, the administrator and obtaining information about the network user from him. Based on the results of the study of network specifics, general recommendations for working with any type of network are offered.

Delegitimising Potential of the “Friend-Foe” Axiological Opposition (Exemplified by English-Language Political Internet Comments)

The article considers the axiological opposition “friend-foe” on the material of non-professional political discourse. English-language comments to the posts of Bill de Blasio, Mayor of New York City, and Sadiq Aman Khan, Mayor of London, published in 2016–2021 in social networks are analyzed in the linguapersonological aspect. The speech techniques of expressing the “friend-foe” binary used in discrediting the current government by users of social networks are revealed. The authors of the comments use various means of verbal aggression to undermine the legitimacy of the authorities. At the lexical level, invective and pejorative units are used. At the grammatical level, the users have recourses to imperative constructions and the superlative degree of adjectives with negative evaluation. Internet users’ comments to politicians’ posts in social networks are shown to form the image of a political actor on the basis of the dominants ‘deviant behaviour’, ‘bad manager’, ‘foe, not their own’, which are determinants in discrediting the authorities. Delegitimising characteristics-determinants used in American and British non-professional political discourse are revealed to be similar due to the general unification and globalization trends. When writing a negative response, the universal institutional characteristics of a politician come to the fore. The unique individual and personal characteristics of mayors are also found to be important in creating a more voluminous image of an illegitimate leader.

Social Media Use: A Cognitive Dissonance Analysis of Heavy Users and Engagement Dynamics

Abstract Purpose – This research investigates the correlation between the utilization of social networking sites, user engagement, and cognitive dissonance. It explores the mediating influence of e-compulsive buying behavior (ECBB) in the connection between SNS use, user engagement, and cognitive dissonance, while also examining the moderating impact of self-esteem on SNS use, user engagement, and ECBB. Methodology – Utilizing purposive sampling, this study collected cross-sectional data from 232 female participants actively engaged in social networking sites (SNSs) and who had made 4-5 online purchases during the last six months. Data were gathered through an electronically administered questionnaire. Descriptive data analysis was conducted using SPSS version 21, while SmartPLS version 4.0 was employed to test the proposed hypotheses. Findings – The findings indicate a significant association between the utilization of social networking sites (SNSs) and user engagement with cognitive dissonance and ECBB. Additionally, it was observed that self-esteem played a moderating role in the relationship between SNS use, user engagement, ECBB, and cognitive dissonance, revealing a heightened impact of cognitive dissonance at lower levels of self-esteem. Practical Implications – These results carry implications for implementing interventions aimed at mitigating the adverse consequences associated with social networking site (SNSs) use, thereby fostering favorable mental health outcomes within the community. Moreover, this study offers valuable insights for managers seeking to enhance their social media strategies by analyzing the varied impacts of social media content and understanding the dynamics between posts to engage both active and passive users effectively. Originality/Value – This study contributes to the limited body of empirical research by examining the correlation between social networking site (SNSs) use, user engagement, and cognitive dissonance through the lens of ECBB. Additionally, in response to recent calls from various empirical studies in this field, the inclusion of self-esteem as a moderating variable predicting cognitive dissonance enhances the novelty of this research.

Performance analysis of shared relay CR-NOMA network based on SWIPT

With the development of wireless communication technology, the number of devices accessing the communication network is increasing. This paper addresses critical issues such as low spectrum resource utilization and the energy constraints of devices. The investigation focuses on the system performance of the shared relay Cognitive Radio Non-Orthogonal Multiple Access (CR-NOMA) network based on Simultaneous Wireless Information and Power Transfer (SWIPT) network model. Unlike the existing CR-NOMA model in which the secondary network user do not participate in the transmission of information from the primary network, we consider the secondary network near user as shared relay. The shared relay utilizes SWIPT technology to harvest energy using a nonlinear energy harvesting model. Additionally, the shared relay assists in transmitting information from the primary network base station to primary user, as well as from the secondary network base station to far user of the secondary network. Subsequently, we conduct a series of simulations to analyze the effects of Signal-to-Noise Ratio (SNR), power distribution factor, interference threshold, and time-switching (TS) factor on system performance. Furthermore, we compare and analyze the performance of our proposed network model against CR-NOMA network across three dimensions: outage probability, throughput, and energy efficiency. Our results demonstrate that the proposed network model exhibits superior outage performance and enhances user throughput compared to the CR-NOMA network. Additionally, it demonstrates improved energy efficiency compared to the shared relay CR-NOMA network, leading to an overall improvement in network performance.

Linking boundary organizations to co-produce actionable knowledge: A case study of ice forecasting for Great Lakes navigation

Abstract Navigating the Great Lakes during icy conditions poses significant safety challenges for the shipping sector. Available ice information is uncertain and fragmented, and navigators must seek out multiple sources for information at the spatial and temporal scales they require, if the information is available at all. Navigators have expressed that they require more highly localized and easily usable information for current and predicted ice conditions to support decision-making. In this study, we seek to meet this information need by applying a boundary organizations chain (BOC) approach to facilitate the co-production of an actionable short-term Great Lakes ice forecast. We focus on two main aspects of this research: 1) producing an actionable decision-support product that meets the needs of Great Lakes ice navigators, and 2) contributing to the knowledge co-production scholarship on BOCs by providing a detailed account of our methods to create a BOC and co-produce an actionable ice forecast. Our results support incorporating existing communities of practice (COPs) into BOCs to enhance the co-production of actionable knowledge, specifically through increasing their complementarity and embeddedness. COPs are informal networks of users that meet voluntarily to share knowledge and develop professional skills, which we found naturally builds the co-production capacities of participants (e.g. embeddedness and complementarity). We also find that COP members are well positioned to disseminate co-produced knowledge across wider user groups.

How position in the network determines the fate of lexical innovations on Twitter

This study analyzes the diffusion of lexical innovations on Twitter to understand how the social network position of adopters impacts their success. Looking at both successful and failed neologisms, we categorize them into "changes" which become established and "buzzes" which decline over time. Using a corpus of 650 million French tweets, we reconstruct user networks and characterize adopters of innovations during different diffusion phases based on prestige, centrality, clustering, and external ties. In the early innovation phase, change and buzz adopters have similar peripheral profiles. During propagation, changes spread to prestigious, central individuals while buzzes do not, which predicts their eventual success or failure. By the establishment phase, changes reach highly central users with closer external ties. The results align with sociolinguistic theories about weak ties for innovation and strong ties for establishment. Additionally, logistic regression models based on early adopter profiles can predict the fate of innovations. This work sheds light on the diffusion dynamics of online lexical innovations and the crucial role of user network factors.

The credibility assessment of Twitter/X users based organization objectives by heterogeneous resources in big data life cycle

Social network data, such as Twitter/X, is of Big Social Data type. Big social data describes people's social behaviors and interactions. They have high business value for decision-making in organizations. However, because of the anonymous nature of social network users, their credibility is ambiguous. Credibility expresses the accuracy and value of big social data. Despite extensive research on the credibility of big social data, most methods have not paid sufficient attention to the important dimensions of their assessment, including user expertise based on topic, selecting social network features, and labeling them. Furthermore, these methods cannot manage the time, high volume, and speed of big social data. To address these issues, this paper presents a novel model for assessing the credibility of Twitter/X users by integrating Twitter/X with Google Scholar. The model automatically defines users' credibility labels using Google Scholar. Machine learning feature selection methods also select features that affect the credibility of Twitter/X users based on the topic. This study uses Google Scholar and the BerTopic algorithm for effective topic modeling on Twitter/X. The model considers unrelated data management, dynamic user credibility, and organizing activities based on the Big Data lifecycle. Finally, using Linear Regression, Support Vector Regression, K-Nearest Neighbor, Random Forest, Classification and Regression Trees algorithms, the model predicts the credibility of Twitter/X users and proves that it performed better than similar models through Classification and Regression Trees. In addition, the model is generalizable for all organizational purposes due to the integration of heterogeneous resources and feature selection methods.

Control of false information in social networks: A short review of communication mechanisms and management strategies

In the digital era, social networks serve as critical platforms for information dissemination but are also plagued by the spread of false information, which can undermine public trust and incite societal discord. This study examines the dynamics of false information dissemination on social networks, including its types, influential factors, and detection and management strategies. We explore various forms of false informationsuch as impersonation, misleading content, and AI-generated forgeriesand analyze the role of user interactions, network topology, and macro factors in the spread of misinformation. Detection methods are reviewed, highlighting advancements in technologies like deep learning, and management strategies are proposed, including user behavior regulation and dissemination path control. Challenges related to legal, ethical, and privacy issues are discussed, alongside the complexities of user behavior and future research directions. The findings underscore the need for comprehensive, adaptive approaches to safeguard the integrity of online information ecosystems.

A distribution network carbon metering method and user carbon emission characterization method considering the influence of harmonics

Abstract This paper presents a novel carbon metering method for distribution grids that fully considers the impact of harmonics on the grid. Harmonics are sinusoidal waves with frequencies that are integer multiples of the fundamental frequency, and the presence of harmonics can lead to power losses, damage to equipment and degradation of power quality, thus increasing carbon emissions from the grid. Currently available carbon measurement methods often do not take this into account, so a more accurate method is needed to assess the carbon emissions of distribution grids. This new method, based on the carbon flow theory of trending power distribution, combines harmonic energy correction and node carbon potential correction, and is able to accurately calculate the carbon emissions as well as the harmonic carbon correction for each node and branch in the distribution network. In order to evaluate the carbon behavior characteristics of users more comprehensively, the study also adopts the DTW-K-Means clustering technique to group distribution network users. This innovative approach provides an effective technical means and theoretical basis for the monitoring, assessment and management of distribution network carbon emissions. This innovative method provides effective technical means and theoretical basis for distribution network carbon emission monitoring, assessment and management, not only considering the impact of harmonics on power metering, but also more accurately portraying the carbon behavior of users, which is expected to provide strong support for the reduction of grid carbon emissions.

Fractal information dissemination and clustering evolution on social hypernetwork

The complexity of systems stems from the richness of the group interactions among their units. Classical networks exhibit identified limits in the study of complex systems, where links connect pairs of nodes, inability to comprehensively describe higher-order interactions in networks. Higher-order networks can enhance modeling capacities of group interaction networks and help understand and predict network dynamical behavior. This paper constructs a social hypernetwork with a group structure by analyzing a community overlapping structure and a network iterative relationship, and the overlapping relationship between communities is logically separated. Considering the different group behavior pattern and attention focus, we defined the group cognitive disparity, group credibility, group cohesion index, hyperedge strength to study the relationship between information dissemination and network evolution. This study shows that groups can alter the connected network through information propagation, and users in social networks tend to form highly connected groups or communities in information dissemination. Propagation networks with high clustering coefficients promote the fractal information dissemination, which in itself drives the fractal evolution of groups within the network. This study emphasizes the significant role of “key groups” with overlapping structures among communities in group network propagation. Real cases provide evidence for the clustering phenomenon and fractal evolution of networks.

Security Risk Analysis in Accounting Information Systems Based on Data Dashboard from the Palo Alto Network PA-820 UTM

This study analyzes security risks in accounting information systems using data from the Palo Alto Network PA-820 UTM dashboard, which monitors various aspects such as network activity, user activity, and application usage. The data includes information on data transfers, source and destination IP activities, and system rule usage. The study aims to identify potential threats that could affect the integrity and security of accounting information systems. Keywords: Accounting Information Systems, Security, Risk, Dashboard, Network Activity.

Enhancing 5G energy efficiency through digital twin networks: A comprehensive review

AbstractIn the era of 5G, seamless mobility handovers are critical in densely populated regions like Malaysia to mitigate disruptions and inefficiencies. 5G networks offer unprecedented data speeds and reliability, essential for advancing mobile communication and Internet of Things applications. However, ensuring continuous connectivity and service during mobility remains challenging, especially in urban settings. Digital twin technology presents a promising solution to enhance 5G handover mechanisms. A digital twin network (DTN) mirroring Malaysia's 5G infrastructure is proposed, utilising real‐time data and user behaviour insights to optimise energy consumption during handovers. The focus is on energy‐efficient protocols and algorithms, reviewed through a systematic literature review. The DTN aims to enhance mobility handover efficiency through predictive handovers and adaptive resource allocation, bolstered by sustainable practices such as edge computing. The potential of DTNs to optimise 5G handover processes is explored, starting with the foundational concepts of 5G mobility and digital twins, highlighting the need for improved strategies in high‐mobility scenarios. Methodologies leveraging digital twins to predict network conditions, simulate handover scenarios, and proactively manage decisions are examined, reducing latency and packet loss. Case studies demonstrate how digital twins adapt dynamically to network changes and user mobility, thereby improving quality of service and user experience. Malaysia's specific 5G mobility challenges are addressed with a tailored DTN emphasising energy efficiency, evaluated through practical applications. Evaluation criteria assess effectiveness with in‐depth analysis of methods, performance metrics, limitations, and recommendations for future research. Challenges and future directions including scalability, security, and real‐time data processing, are discussed, aiming to integrate digital twin technology with 5G networks for enhanced connectivity. This abstract provides a roadmap for leveraging digital twins to optimise 5G network performance sustainably, guiding future research and implementation strategies.