About One Approach to Assessing the Social Networks' Users Influence

The appearance of social networks has been one of the most important events in the last decade. A social network is a network of interaction and communication whose nodes are its members and its edges are the relation between them. An outstanding concept in social networks is the concept of social influence which is of great significance. Social influence is the behavioral transformation in a person, caused by his/her relationship with other people, organizations and the network. Today, the concept of social influence is used in viral marketing. The aim of viral marketing is to find a small subset of influential users in the social networks for marketing a product. The aim of this paper is to offer an algorithm in order to choose expert and influential users in social networks. This algorithm uses leader discovery method in social networks for choosing expert and influential users. This method chooses those users as the expert and influential leaders of the social networks, who are influential among the other members and also those users that have enough specialty and knowledge about the marketing product. Consequently, the process of selecting expert and influential users for the algorithm would be quick and also the selected leaders are influential and expert enough to do marketing and advertising in the social network.

Identification of influential users by neighbors in online social networks

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.

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 purpose of research was to study the existing methods of determining the degree of cohesion of two users of social network, identifying their shortcomings and developing a new method. The research identified shortcomings of existing methods and proposed a new method for assessing the degree of cohesion of social network profiles based on open data from a social network. Under the degree of cohesion of users’ profiles is understood the probability of communication (interaction) of profile owners in real life, it is calculated for two users of the social network and expressed in percent. The work of the method is demonstrated on the example of the social network “In contact”. This method includes the sequence of the following stages: the first stage is data collection about users of the social network with API and the formation of tuples of users’ profile characteristics. A tuple of characteristics of social network profiles is the data, collected for each user, stored in a structured form. The next step is the analysis of the collected information. For each characteristic of the tuple of profiles, i.e. the possible element of interaction of users in the social network, the coefficient of cohesion by the characteristic is calculated. In addition, for each feature, its informativeness is calculated, i.e. how important is this or that feature in this social network. At the final stage, the results are generated, using the formula for the probability of communication between two users, derived during the investigation. Obtained as a result of the application of the method, the probability of communication between two users can be used to optimize the activities of the operative-search services and special bodies. In addition, the received degree of cohesion of two users can be interpreted as the probability of a channel of information leakage between them. The role of the user of the method can be any private or state organization that cares about the security of corporate data and commercial secrets, the operative-search service, as well as an organization that investigates cybercrimes and information security incidents.

The smart grid is developing in the direction of two-way interaction. As an important platform for two-way interaction between the user side and the power system, the social network plays an important role in the development of the smart grid. The structural parameters of the social network include the total number of users and the network density. The network density is reflected by the number of neighbor nodes of the network node and the strength of the connection. Determining the total number of users in the network is beneficial to the smart grid for power allocation. Power system load peaks, insufficient power supply, etc. Deter-mining network density helps speed up the popularization and promotion of smart grid technology and helps to understand the social influence of users in social networks. Offline social network topologies are usually selected by interviewees and then asked to list their neighbors. This paper proposes an inference technique based on so-called “fixed choice” surveys, which requires the interviewee to enumerate all the strong ties and a fixed number of weak ties(e.g.,two or ten). Then use the method of moments to infer the total number of users and the network density of the social network. Experiments using simulated data show that the proposed inference algor-ithm performs well in various network topo-logies and measurement scenarios, and the est-imates obtained are significantly more accu-rate than the estimates obtained directly using the coarse observation network.

Using multimodalities, the field of biometric is receiving widespread acceptance for efficient communication between users in social network. Hence, suitable designing of authentication and access control is highly required to perform analysis of system in social network domain. In this work, a Proximal Spatial Vector and Affinity Coefficient (PSV-AC) framework is designed with the objective of ensuring authentic communication between users in social network using face and fingerprint. Initially, multimodal user biometric features with proximal feature pixel stored in spatial vector are gene encoded to obtain biometric identity keys. User authenticity is authorized by decoding gene encoded biometric features using biometric identity keys. User Affinity Coefficient is calculated via antecedent instances shared between different social users. The social network user’s affinity coefficient is ordered in a matrix. Matrix ranking is then performed to identify whether authentic user social communication happened in network. Experiments have been carried out using BioSecure datasets to measure the performance in terms of number of social users, social network authentication time, access control time, multimodal biometric feature template size, and true positive rate.

Social networks (SNs) have become an integral part of contemporary life, as they are increasingly used as a basic means for communication with friends, sharing of opinions and staying up to date with news and current events. The general increase in the usage and popularity of social media has led to an explosion of available data, which creates opportunities for various kinds of utilization, such as predicting, finding or even creating trends. We are thus interested in exploring the following questions: (a) Which are the most influential - popular internet publications posted in SNs, for a specific topic? (b) Which members of SNs are experts or influential regarding a specific topic? Our approach towards answering the above questions is based on the functionality of hashtags, which we use as topic indicators for posts, and on the assumption that a specific topic is represented by multiple hashtags. We present a neighborhood-based recommender system, which we have implemented using collaborative filtering algorithms in order to (a) identify hashtags, urls and users related with a specific topic, and (b) combine them with SN-based metrics in order to address the aforementioned questions in Twitter. The recommender system is built on top of Apache Spark framework in order to achieve optimal scaling and efficiency. For the verification of our system we have used data sets mined from Twitter and tested the extracted results for influential users and urls concerning specific topics in comparison with the influence scores produced by a state of the art influence estimation tool for SNs. Finally, we present and discuss the results regarding two distinct topics and also discuss the offered and potential utility of our system.

Purpose As social networking services are popular, the need for recognizing reliable people has become the main concern. Quantifying the relationships between the users of social networks is also one of the important challenges with the increasing number of users. So, trust plays an important role in social networks in order to recognize trustworthy people. The purpose of this paper is to propose an approach to recognize trustworthy users and protect users from abuse by untrustable users. Design/methodology/approach In this paper, the authors suggest a method to measure the trust value between users of social networks based on call log histories such as abundance, novelty, and sincerity and quality of service (QoS) requirements such as accessibility, response ability, tend to respond, and agility. After that, the authors calculate error-hit, precision, and recall to evaluate the trust value. Findings The results indicate that the proposed approach has better performance in terms of error-hit, precision, and recall than FIFO, combined, QoS-based, and call log-based method. Originality/value In this paper, the trust issue in social networks is pointed out and a new approach to evaluate the trust value is proposed based on call log histories and QoS requirements.

Social network services (SNSs) are fundamentally transforming the way businesses communicate with customers. Key issues in understanding social media and such services include how to generate more information traffic and how to increase information exposure. Unlike previous studies in the research stream studying patterns of information diffusion, we investigate the role of influential Twitter users, focusing on their positions in social networks, in redistributing contents generated by other users. To do this, we employ sociological theories in determining which characteristics of connecting users provide more influence. Specifically, a connecting user's position in networks observable to audience affects the user's influence in a way that connecting users with relatively high status and those embedded in a closely connected community exert the greatest impact on the network. Moreover, the value of a connecting user's network position can be contingent on the source user's status and network position: a high-status connecting user contributes more to information diffusion when tweets are generated by a low-status source user, and a connecting user who is embedded in a well-connected network contributes more to information diffusion when tweets are generated by a source user in a brokerage position. After exploring data on tweets about Apple products and services, we find evidence that support this set of theoretical claims. Such findings would also provide a useful guidance for practitioners to enhance information diffusion by effectively designing the sequence of content recipients based on their network positions.

Social networks have become an important marketing tools for business to build brand pages to prompt their new products. Fans' user-experience diffusion results in great marketing power that people never seen. Typically, some of the fans in group are influence users. They are market movers which mean they can influence others buying decisions. Businesses can affect online influence users by giving them extra benefits to turn them into spokesmen. However, who is the influential user? What period of time is appropriate for information to spread? In this study, a framework based on frequent pattern mining is proposed to find the influence users as well as the proper time to spread information. The one day 24-hour period can be divided into successive time segments. An influence transaction that contains fans' influence power will be defined in each time segment. After transactions being collected several days, the frequent patterns can be found to deduce the proper time for influence users to spread information. The theoretical experiment is given to show how the proposed framework works.

ABSTRACTWith online social networking having revolutionized the way in which individuals communicate and interact with each other, there is heightened research interest in the dynamics of social networks. This article seeks to contribute to this stream of research by addressing the key question of the impact of major life events, such as getting married or graduating from college, on social network evolution. Consistent with prior studies on the evolution of individuals’ social networks, we specifically focus on two key attributes of an individual's network: indegree of ties and relational embeddedness. By longitudinally analyzing the network activities of a large‐scale online social network, we find that the indegree of ties increased significantly following a major life event, and that this impact was stronger for more active users in the network. Interestingly, we also find that the broadcast of major life events served to revive dormant ties as reflected by a decrease in embeddedness following a life event. We also found that one‐time life events such as weddings had a greater impact than recurring life events such as birthdays on the evolution of a user's social network. From a research perspective, our study contributes to existing research by focusing on a user's communication network as opposed to friendship network and by emphasizing how exogenous life events add a different dimension to user communication patterns. The importance of life events on social network evolution has important implications for practice as well by providing insights to advertisers as well as to social networking sites.

Computational trust in the social media depends both on the interaction types and on the expertise, which exposes user’s interests by tags on items such as books, articles, images etc. However, when there is not any interaction among users, such a computation is impossible. In this paper, we first propose a novel model of computational trust among users in social network to incorporate the interaction among users and semantics of topics based on tags posted by users. Both types of direct and indirect interaction via intermediate ones are utilized for computation in the model. Then, we introduce algorithms for computing trust among users in social network.

This book examined the influence of the new, revolutionary Social Networking (SN) tool on the education sector nationally and internationally. SN usage by businesses and individuals has become a significant instrument for searching, conducting research, communication, entertainment, commerce and information, as well as teaching. SN was introduced to simplify communication, collaboration, contribution, creativity and interaction among individuals/groups within the same sector worldwide. SN has become an essential part of operations in various sectors including business enterprises, health and education. The use of SN in the education sector can bring diverse opportunities and risks to enhance students’ engagement with their colleagues and teachers universally; however, this technology can bring some risks from four perspectives: (1) Cognitive Development, (2) Social Development, (3) Physical Development and (4) Security. This book aimed to explore the opportunities and risks of using SN in relation to various countries from regions including Asia Pacific, Europe, Mediterranean, America, Middle East and the Caribbean. Furthermore, a Social Networking and Education Model (SNEM) is developed to promote and implement Social Networking in the education sector.

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