A Novel Negative Link Prediction Algorithm for Social Networks

Traditional tools and software for social network analysis are seldom scalable and/or fast. This paper provides an overview of an R package called fastnet, a tool for scaling and speeding up the simulation and analysis of large-scale social networks. fastnet uses multi-core processing and sub-graph sampling algorithms to achieve the desired scale-up and speed-up. Simple examples, usages, and comparisons of scale-up and speed-up as compared to other R packages, i.e., igraph and statnet, are presented.

With the development of increasingly sophisticated technology and communication, social media tends to be used as the main pillar in delivering information digitally, communicating in business, helping product marketing, communicating with customers and suppliers, and reducing costs that must be incurred conventionally. Social media that are usually used are those that are open and whose existence is recognized by the wider community and are made in various forms of platforms with a structured system or known as social media networks. One of the users of the social media network is household MSME actors, especially those affected by Covid-19 which has caused a decrease in the number of buyers in MSME businesses. With easy and fast access to social media networks, MSME actors are expected to be bolder in promoting their products because there is no time limit on their use and the wide range of product marketing areas to the global market, thus making social media networks an effective marketing medium. However, household MSME products still have obstacles in penetrating the export market, namely obstacles in quality standards. To overcome this, household MSME actors need to understand and apply marketing strategies that support efforts to increase competitiveness by using social media networks in the face of increasingly fierce business competition. MSME actors must also make extensive use of social media networks in developing their businesses so that they can progress quickly and be ready globally. In this article, the discussion focuses on the importance of understanding and applying marketing strategies for household MSME products on social media networks.

Link prediction is an essential research area in social network analysis. In recent years, link prediction in signed networks has drawn much concentration of the researchers. To predict potential positive and negative links, we should predict not only the existence of the link between the nodes, but also the sign and the probability of the existence of the link. In addition, the link prediction result should satisfy the social balance and status theories as much as possible. In this paper, we propose an algorithm for link prediction in signed networks based on latent space mapping. Taking the social balance and status theories into consideration, we define a balance/status coefficient matrix to reflect the balance/status constrains on the signs of the unknown links. We also present the concept of signed degree ratio and the signed degree ratio-based similarity between the node pairs to measure probability of the signed links. We propose a latent space-based model for the connections in a signed network which combines the topological structure and the balance/status constrains. An alternative iteration algorithm is proposed to optimize the model, and its convergence and correctness are proved. By this alternative iteration method, time complexity of our algorithm is reduced greatly. Empirical results on real world signed networks demonstrate that the algorithm proposed can achieve higher quality predicting results than other algorithms.

It is obvious today that a new era of communication and interaction has been started through social media networks. For this reason, corporate social media networks and their contents are one of the correct and effective promotional tools that can be used in destination promotion. Based on this situation, the main purpose of the study is to examine the use of social media networks by central and local governments in Trabzon destination, specifically for destination promotion. Within the framework of this purpose, the corporate Facebook, Instagram and Twitter social media accounts of Trabzon destination center and local governments are analyzed with the quantitative content analysis method. As a result of the study, it is determined that Trabzon destination center and local governments do not use social media networks effectively for destination promotion. Additionally, it is concluded that the majority of the promotion-oriented posts on Facebook, Instagram and Twitter accounts are made by the Trabzon Provincial Directorate of Culture and Tourism.

The motivation for this paper arises from the importance of stakeholder communications for firms and the rapid emergence of social media networks as a means of such communications. Our purpose is to understand the early adoption and use of social media networks for such communications by firms at a point in time when such networks were relatively new. Specifically, we examine the adoption and use of Facebook (the most popular social networking site) by franchisors and focus on two questions: (1) antecedents of early adoption of Facebook by franchisors, mainly in terms of system age, size, organizational form, advertising royalty rates, industry, and internationalization; and (2) variations in use of Facebook (in terms of content) by these franchisors and the way these variations relate to the characteristics of the franchisors and their systems. Our empirical study covers 408 franchisors doing business in the French market. The analysis approach is quantitative, with the use of t‐tests, Pearson chi‐square tests, and a logistic regression model as well as a content analysis of the franchisors' Facebook pages. At the time we collected data, we found that only 21.3 percent of the franchisors had a presence on Facebook. Several characteristics of the system (e.g., percentage of company‐owned outlets, advertising royalty rates, and industry) influence whether a franchisor has a presence on Facebook as well as the nature of content on the relevant Facebook page. We draw on organizational theories and institutional economic theories to offer potential explanations for our preliminary investigation. These research findings can help the franchisors better understand the role of social media networks for stakeholder communications. In particular, our results offer insights for smaller franchisors, in terms of when and how they adopt communication innovations to compete against larger firms.

PurposeUsing the context of Rio Olympic games, the purpose of this paper is to investigate attitude toward sponsorship outcome as it relates to purchase behavior, gender, sponsor patronage, sports enthusiasm, and social media consumption.Design/methodology/approachData were collected using an online survey of 265 participants. Questions regarding demographics, viewing habits, sports participation, enthusiasm, attitude toward Olympic events were included in the survey. The four sub-scales were sponsorship attitude, sponsor patronage, social media consumption, and sports enthusiasm.FindingsThe findings of the study showed that social media consumption is positively related to attitude toward event and sports patronage. There was a significant gender difference on attitude toward event, social media consumption, and sports enthusiasm. Predictors for making a purchase as a result of seeing a social media advertisement were gender, playing competitive sport, and social media consumption.Practical implicationsThis study will add to the body of academic and practitioner research on sponsorship outcomes, and provides an opportunity for marketers to leverage social media networks for sponsorship communication.Originality/valueAs the use of social media networks has increased over the past few years, no previous study has investigated association of sports enthusiasm, gender, or social media consumption toward sponsor patronage which relates to consumers seeking out sponsors and being influenced to make a purchase as a result of marketing communication of sponsors.

Link prediction in multilayer networks using weighted reliable local random walk algorithm

Users' addiction to online social networks is discovered to be highly correlated with their social connections in the networks. Dense social connections can effectively help online social networks retain their active users and improve the social network services. Therefore, it is of great importance to make a good prediction of the social links among users. Meanwhile, to enjoy more social network services, users nowadays are usually involved in multiple online social networks simultaneously. Formally, the social networks which share a number of common users are defined as the "aligned networks". With the information transferred from multiple aligned social networks, we can gain a more comprehensive knowledge about the social preferences of users in the pre-specified target network, which will benefit the social link prediction task greatly. However, when transferring the knowledge from other aligned source networks to the target network, there usually exists a shift in information distribution between different networks, namely domain difference. In this paper, we study the social link prediction problem of the target network, which is aligned with multiple social networks concurrently. To accommodate the domain difference issue, we project the features extracted for links from different aligned networks into a shared lower-dimensional feature space. Moreover, users in social networks usually tend to form communities and would only connect to a small number of users. Thus, the target network structure has both the low-rank and sparse properties. We propose a novel optimization framework, SLAMPRED, to combine both these two properties aforementioned of the target network and the information of multiple aligned networks with nice domain adaptations. Since the objective function is a linear combination of convex and concave functions involving nondifferentiable regularizers, we propose a novel optimization method to iteratively solve it. Extensive experiments have been done on real-world aligned social networks, and the experimental results demonstrate the effectiveness of the proposed model.

With the continual rise of online and hybrid modes of learning, students collaborating through social media networks (SMN) is becoming increasingly important. Yet, students are struggling to unleash the potential of SMN to support their learning. This study investigated how SMN can be effectively used for collaborative learning in higher education through using a mixed methodology. Findings showed that SMN's effective use influenced students’ collaborative learning goal attainment and was facilitated by perception of SMN affordances and social capital. This thesis extends the theory of effective use by proposing an approach for contextualizing effective use of SMN for collaborative learning.

The aim of this study is to reveal Open Education related social media usage in Turkey through social network analyses. To this end, the most widely used social media network in Turkey, Facebook, was chosen. All the pages and groups created on Facebook related to Open Education were found. A total of 207 groups and 521 pages were accessed and entered into the social network analysis. Within the scope of this study, the density and centrality features of the network were analyzed for groups whereas the density, centrality, and degrees features of the network were analyzed for pages. The study's results revealed that while pages and groups related to programs emphasizing current job opportunities and student support services were more actively used, commercial accounts attempted to exploit students' exam anxiety instead of generating actual content. The findings also revealed that well structured institutional pages appeal to students and have the potential to reach high levels of popular in short periods of time.

Nowadays, the user linkage or link prediction task is usually based on user profile or some sensitive data (i.e., name, gender, ID, life or health insurance accounts, etc.). With the enhancement of laws and regulations, the difficulty of personal sensitive data acquisition is increasing. Moreover, the abnormal users called online water army often camouflage themselves to achieve specific goals. They often register false user information such as name, gender, age, etc. To protect privacy and satisfy the needs of camouflage, users and ISPs often hide those sensitive data (i.e., user profile). In this paper, we want to link same user in multiple social networks, which is formally defined as ULASN (User Linkage across Anonymized Social Networks) problem. ULASN is very challenging to address due to (1) the lack of enough ground-truth to build models and obtain accurate prediction results, (2) the studied networks are anonymized, where no user profile or sensitive data is available, and (3) the need of scalable algorithms for user linkage task in large-scale social nateworks, and (4) users in social network are interrelated. To resolve these challenges, a noval user linkage framework based on social structures called ULA is proposed in this paper. ULA tackles these problems by considering massive, low-quality and interrelated user information. It uses few ground-truth to partition users into blocks, which reduces the size of candidates. By extending Fellegi-Sunter methods, our proposed algorithm can handle social network similarity complying to continuous distributions. A probabilistic generative model is proposed and solved by EM algorithm. Simultaneously, missing value problem can also solved when we use EM algorithm to learning parameters. Extensive experiments conducted on two real-world social networks demonstrate that ULA can perform very well in solving ULASN problem.

The use of social media, such as social networking sites continues to grow in everyday life, along with their use in the workplace. So, a concern raised about how the personal use of social networking sites like Facebook, WhatsApp, Instagram, etc., at work affects employee job performance. Moreover, the purpose of this study is to explore the relationship between social networking usage and employee job performance through the mediating role of work–life balance. The quantitative methodology was employed in this study. Data was collected using a self-administered questionnaire adapted from a previously validated survey. The target population consisted of Gen-Y employees of the corporate sector in Chandigarh, Delhi, and Haryana (UTs/states) of India. Judgemental sampling was applied to collect data from 224 employees of the corporate sector. The study results indicated that time spent, frequency of using social networking sites has significant relationship with job performance and work–life balance of employees while work–life balance and employee job performance are significantly positively related. However, social networking use has a significant negative relationship with job performance and work–life balance. Additionally, work–life balance significantly mediates the relationship between social networking usage and job performance. Therefore, the study concluded that employees struggle to set boundaries since they cannot distinguish between work and family life due to the rise in technology and social media use. Therefore, managers and HR professionals need to consider the negative aspect of online social networking at work. Further, the implications for managers and organizations are discussed.

With the advent of Web 2.0 in the very recent years, we are witnessing an attitude change in the use of the World Wide Web, which is not used anymore to only retrieve information but to allow participation, communication, collaboration, and information sharing on the Web. This has led to the dramatic grow of social media networks, among other services. In this position paper the main privacy concerns related to the use of social media networks are highlighted and some procedural as well as technological privacy sympathetic approach to social multimedia networking are given. © 2009 IEEE.

Grouping users in social networks is an important process that improves matching and recommendation activities in social networks. The data mining methods of clustering can be used in grouping the users in social networks. However, the existing general purpose clustering algorithms perform poorly on the social network data due to the special nature of users' data in social networks. One main reason is the constraints that need to be considered in grouping users in social networks. Another reason is the need of capturing large amount of information about users which imposes computational complexity to an algorithm. In this paper, we propose a scalable and effective constraint-based clustering algorithm based on a global similarity measure that takes into consideration the users' constraints and their importance in social networks. Each constraint's importance is calculated based on the occurrence of this constraint in the dataset. Performance of the algorithm is demonstrated on a dataset obtained from an online dating website using internal and external evaluation measures. Results show that the proposed algorithm is able to increases the accuracy of matching users in social networks by 10% in comparison to other algorithms.

Analysis of opinion dynamics in social networks plays an important role in today's life. For applications such as predicting users' political preference, it is particularly important to be able to analyze the dynamics of competing opinions. While observing the evolution of polar opinions of a social network's users over time, can we tell when the network "behaved" abnormally? Furthermore, can we predict how the opinions of the users will change in the future? Do opinions evolve according to existing network opinion dynamics models? To answer such questions, it is not sufficient to study individual user behavior, since opinions can spread far beyond users' egonets. We need a method to analyze opinion dynamics of all network users simultaneously and capture the effect of individuals' behavior on the global evolution pattern of the social network. In this work, we introduce Social Network Distance (SND) - a distance measure that quantifies the "cost" of evolution of one snapshot of a social network into another snapshot under various models of polar opinion propagation. SND has a rich semantics of a transportation problem, yet, is computable in time linear in the number of users, which makes SND applicable to the analysis of large-scale online social networks. In our experiments with synthetic and real-world Twitter data, we demonstrate the utility of our distance measure for anomalous event detection. It achieves a true positive rate of 0.83, twice as high as that of alternatives. When employed for opinion prediction in Twitter, our method's accuracy is 75.63%, which is 7.5% higher than that of the next best method. Source Code: https://cs.ucsb.edu/~victor/pub/ucsb/dbl/snd/