Modeling Spread of Preferences in Social Networks for Sampling-Based Preference Aggregation

The analysis of social networks, which focuses on the relationship between social entities today is an area of active research. It is a set of tools for research, in particular, in combination with artificial intelligence methods such as machine learning, deep learning. The paper examined the current quality of the assessment of information in social networks, analyzed the methods of searching and sorting information in various social networks, as well as the process of providing recommendations to users. Social media data is an inexhaustible source of research and business opportunities. In general, social media data is information gathered from social networks that shows how users interact with content. Methods of improving search results for personalizing recommendations in social networks are given. These indicators and statistics provide an effective understanding of the strategy of behavior in social networks. The advantages and disadvantages of a multifactor assessment system are considered. The possible ways of integrating the combined system of evaluating information elements by the user to optimize search queries and filtering big data are identified.

Despite increasing attention to the influence of customer involvement and social media in developing innovation in small- and medium-sized enterprises (SMEs), few studies have specifically investigated how to improve the effectiveness of social media–based customer involvement. Drawing on the knowledge-based view, social network and capability theories, we address this question by arguing that Social Customer Relation Management (CRM) enhances the effect of social media–based customer involvement upon innovation. Two key capabilities further enhance the moderating effect of Social CRM: social media network and customer information processing capabilities. The results from a longitudinal dataset of 317 SMEs indicate that SME customer involvement, enabled by Social CRM, social media network and customer information processing capabilities increases innovation. These findings have important implications for researchers and managers interested in enhancing the efficacy of SME customer involvement using social media.

Social networks during the transition to adulthood from the perspective of Israeli care leavers and their social workers

A novel negative feedback information dissemination model based on online social network

This dissertation consists of two original studies in social networks and one original study in political economy. In the first two chapters, I study (i) how social networks form, and (ii) how economic agents optimize their behaviors for a given network structure. In the last chapter, I examine how election rules affect individual voting decisions and ultimate election outcomes. In Chapter 1, Social Network Formation and Strategic Interaction in Large Networks, I present a dynamic network formation model that aims to explain why some empirical degree distributions exhibit the increasing hazard rate property (IHRP). In my model, a sequentially arriving node forms a link with one existing node through a bilateral agreement. newborn node prefers a highly linked node; however, the more links an existing node has, the more the marginal return from an additional link diminishes. I prove that the IHRP emerges if and only if the latter effect prevails over the former. I present two implications of the IHRP for strategic interactions in networks. First, when there is uncertainty about neighboring agents' connectivity, the IHRP guarantees that a unique Bayesian equilibrium exists in a network game with strategic complementarities. Second, the IHRP characterizes a monotone revenue-maximizing mechanism with allocative externalities. In Chapter 2, Monopoly Pricing and Diffusion of a (Social) Network Good, I present a model of dynamic pricing and diffusion of a network good sold by a monopolist. In the model, the network good is a subscription social network good. This means that in each period, each consumer has to pay a subscription price to use the good, and the utility derived from subscribing to the good increases as more of her neighboring consumers subscribe. Consumers myopically optimize their subscription decisions, and the monopolist chooses a sequence of subscription prices that maximizes his discounted sum of per-period profits. Three main results emerge. First, I characterize a unique steady state of the monopoly market. Second, I find that optimal sequences of subscription prices oscillate around the subscription price at the steady state as time passes. Third, I analyze how changes in the monopolist's discount factor and the density of the social network affect the subscription price, subscription rate, and deadweight loss at the steady state. In Chapter 3, A Model of Pre-Electoral Coalition Formation, I study how two different election rules, simple plurality (e.g., as in South Korea) and two-round runoff (e.g., as in France), affect political candidates’ incentives to form pre-electoral coalitions (PECs). In my model, three candidates compete for a single office, and two candidates can form a PEC. Since the candidates are both policy- and office-motivated, one candidate can incentivize the other candidate to withdraw his candidacy by choosing a joint policy platform. I find that PECs are more likely to form in plurality elections than in two-round runoff elections. I further examine how other electoral environments, such as ideological distance and pre-election polls, influence incentives to form PECs.

<span lang="EN-US">Social networks provide a fairly wide range of data that allows one way or another to evaluate the effect of the dissemination of information. This article presents the results of a study that describes methods for determining the key parameters of the model needed to analyze and predict the dissemination of information in social networks. An approach based on the analysis of statistical data on user behavior in social networks is proposed. The process of evaluating the main features of the model is described, including the mathematical methods used for data analysis and information dissemination modeling. The study aims to understand the processes of information dissemination in social networks and develop recommendations for the effective use of social networks as a communication and brand promotion tool, as well as to consider the analytical properties of the classical susceptible-infected-removed (SIR) model and evaluate its applicability to the problem of information dissemination. The results of the study can be used to create algorithms and techniques that will effectively manage the process of information dissemination in social networks.</span>

Increasing numbers of smart phone users and Social Network Services (SNS) created a new pattern and characteristic called Social Lifestyle, the way of smartphone users to supply and consume various social media contents. Many SNS services provide individual solutions to group users within a physical location or those who share a common interest in order to exchange and share personal knowledge and contents. Although some of the web-based open platforms are trying to integrate the individual social applications and APIs, there are limitations of sharing social contents and social network information across the separated service providers and platforms. In order to solve such limitations and complexities, there needs to be a generalized model which supports a unified way of social content sharing as well as the system architecture for support the cross social media sharing mechanism. To do so, the paper initially proposes the Place-Content-Social Network based content sharing model (PCS Model) which supports a sophisticated way of connecting user interests for sharing contents within the boundaries of the locations and social network information. The PCS model also provides a social trust for sharing contents based on the analysis of the attributes of the social contents including the user participation, communication patterns and contents sharing activities called the Social Activities.

Introduction states that the creation of the Internet leads to the unification of social networks’ communication norms and rules, as wellas to the development of an artificial language of communication. They facilitate the communication of different nations’ representatives in the globalization and multiculturalism context. The aim of the article is to identify the mutual influence of communication processes in social information networks and the multiculturalism phenomenon. The socio-cultural approach methodology was used to study the social information networks communication peculiarities within the context of cultural transformations. The linguistic means of communication of cultural content were investigated through the method of cultural-semantic analysis. Research results substantiate that the language of communication in various Internet spheres social networks differ both in form and in content. However, the information and communication technologies language uses common characteristics such as emoticons, which affect emotions and feelings. Different information networks keep the same meaning for those characteristics, which makes them understandable for all users alike. The problems of social networks communication in the conditions of multiculturalism are also revealed. They relate to the existence of not one, but several variants of English language (American, Indian, Australian, etc.). Discussion deals with the research results of the philosophers and scientists who to some extent touched upon the problems of social networks communication such asS. Huntington, M. Castells, D. Ivanov, V. Lyakh, L. Orokhovskaya, V. Onoprienko, S. Yagodzinskiy. The differences between the results obtained by the author of the article and the indicated researchers are shown. Conclusion indicates that the use of computer information technologies lead to the Internet communications specific language formation. It allows the Network users to understandeach other through the words, signs, symbols in multiculturalism environment. People learn to make common ideas and interests.

The use of social media is having an increasingly positive impact on tourism activities and the information available on social networks. The aim of this study is to investigate the opinions of social network users regarding their perceptions of social network use, social network communication, and social network information that influence the decision-making process of potential tourists when choosing a destination. The survey was conducted using an online questionnaire and the collected data was processed using the PLS-SEM method. The results show that the use of social networks, communication, and search for information in social networks influence the decision-making process when choosing a destination.

The internet presents a vast labyrinth of information and choices, overwhelming users while service providers strive to predict user preferences accurately. Traditional recommender systems, although utilizing extensive data, often lack the social context necessary for precise recommendations, leading to issues like cold-start and data sparsity. Social network-based recommender systems have been proposed to address these limitations by leveraging the decision-making processes influenced by social connections. Recent advancements in this domain, such as the Diffnet and Diffnet++ models, have shown improved prediction accuracy through deep social influence propagation models. However, their performance diminishes with higher diffusion depths. In this work, we propose enhancements to the Diffnet and Diffnet++ models by incorporating the homophily effect in the heterogeneous graph of the diffusion layer and implementing a weighted embedding method in the prediction layer. Using data from Yelp and Flickr, our improved model integrates higher-order social structures and interest network information, demonstrating superior performance on metrics such as Normalized Discounted Cumulative Gain (NDCG) and Hit Ratio (HR) compared to existing baselines. Our approach effectively integrates social network data and user similarity networks, resulting in more accurate and reliable recommendations for online platforms where social networking is a key feature.

The purpose of this document is to highlight two strategies which increased feedbacks and stimulated the diffusion of information in social networks. Starting by an online survey, which assists searchers in understanding the behavior of social network users, to define the problem. The information received with this survey has been tested on validation by an analyze on the most popular type of posting on social networks. The results of this study provided two strategies as a solution for the problem of dissatisfaction among users of social networks and to increase feedbacks and diffusion of information on social network. One strategy is manual and is based on the type of publication and the second strategy is automatically and takes the concept of collaborative work in to account

The development of the post-industrial society requires the acceleration of the integration of the national economy into the globalized economic space. This stage is characterized by active informatization of all spheres of life in society, which requires information security and cyber protection for high-quality information provision of the country’s population, intellectualization of economic processes and prevention of destructive informational influence on the social status of the individual. It is also necessary to consider that for Ukraine the specified stage is complicated by the hybrid war with Russia, which requires strengthening the protection of information from cyber-attacks and the formation of new approaches in preserving the quality of information on social networks. Therefore, the purpose of this article is the development of scientific and methodological approaches in information security management, strengthening its social significance. This requires solving a certain range of tasks of identifying and preventing socially dangerous information based on the use of economic and mathematical methods and models. The article highlights the main directions of socialization of modern technological development and theoretically substantiates its significant impact on human consciousness and behavior, puts a person in front of serious challenges and, under conditions of hybrid warfare, requires strengthening of information security in social networks. In the work, the components of information security are supplemented, its impact on social aspects of society’s life is highlighted. As a result of the research, the authors proposed a scientific and methodical approach to the construction of a system for countering the spread of socially dangerous information in social networks. Besides, its functional elements are highlighted. A method of combating the spread of harmful information in social networks has been developed, which solves the problem of information support of the decision-making process and includes providing the chosen and alternative options to the person making the decision, with the justification of the choice. This creates prerequisites for information support for making a well-founded management decision.

The main focus of this paper is to propose integration of dynamic and multiobjective algorithms for graph clustering in dynamic environments under multiple objectives. The primary application is to multiobjective clustering in social networks which change over time. Social networks, typically represented by graphs, contain information about the relations (or interactions) among online materials (or people). A typical social network tends to expand over time, with newly added nodes and edges being incorporated into the existing graph. We reflect these characteristics of social networks based on real-world data, and propose a suitable dynamic multiobjective evolutionary algorithm. Several variants of the algorithm are proposed and compared. Since social networks change continuously, the immigrant schemes effectively used in previous dynamic optimisation give useful ideas for new algorithms. An adaptive integration of multiobjective evolutionary algorithms outperformed other algorithms in dynamic social networks.

The real dataset consists of movie evaluations from IMDB, which provides a platform where individuals can evaluate movies on a scale of 1 to 10. If a user rates a movie and clicks the share button, a Twitter message is generated. We then extract the rating from the Twitter message. We treat the ratings on the IMDB website as the event truths, which are based on the aggregated evaluations from all users, whereas our observations come from only a subset of users who share their ratings on Twitter. Using the Twitter API, we collect information about the follower and following relationships between individuals that generate movie evaluation Twitter messages. To better show the influence of social network information on event truth discovery, we delete small subnetworks that consist of less than 5 agents. The final dataset we use consists of 2266 evaluations from 209 individuals on 245 movies (events) and also the social network between these 209 individuals. We regard the social network to be undirected as both follower or following relationships indicate that the two users have similar taste.

Unlike expertise location systems which users query actively when looking for an expert, expert recommender systems suggest individuals without the context of a specific problem. An interesting research question is whether expert recommender systems should consider a users' social context when recommending potential research collaborators. One may argue that it might be easier for scientists to collaborate with colleagues in their social network, because initiating collaboration with socially unconnected researchers is burdensome and fraught with risk, despite potentially relevant expertise. However, many scientists also initiate collaborations outside of their social network when they seek to work with individuals possessing relevant expertise or acknowledged experts. In this paper, we studied how well content‐based, social and hybrid recommendation algorithms predicted co‐author relationships among a random sample of 17,525 biomedical scientists. To generate recommendations, we used authors' research expertise inferred from publication metadata and their professional social networks derived from their co‐authorship history. We used 80% of our data set (articles published before 2007) as our training set, and the remaining data as our test set (articles published in 2007 or later). Our results show that a hybrid algorithm combining expertise and social network information outperformed all other algorithms with regards to Top 10 and Top 20 recommendations. For the Top 2 and Top 5 recommendations, social network‐based information alone generated the most useful recommendations. Our study provides evidence that integrating social network information in expert recommendations may outperform a purely expertise‐based approach.