Search

Public-facing social media platforms, such as Facebook and WeChat, are increasingly being embedded into corporate processes and routines. The use of public social media by employees has aroused widespread interest among scholars in recent years. This study summarises published theories and models and proposes a causal-chain framework for research exploration into employee usage of public social media platforms by systematically analysing the antecedent variables, mediators, moderators, and outcome variables used in 59 quantitative papers. The representative theories include: Social Capital Theory, Job Demands-Resources Model, Boundary Theory, Media Synchronicity Theory, Social Cognitive Theory, Technology Acceptance Model, Self-Determination Theory, and Media Richness Theory. Historically, researchers have studied social media usage behaviours as antecedents, rather than social factors, with many focusing on outcome variables such as job performance and job satisfaction, while the impact of employee social media usage on physical and mental health is less studied. In terms of moderators, variables such as use behaviour, user characteristics and job characteristics receive most attention. With regards to mediators, social capital, job satisfaction, and work conflict are most significant. This study proposes future research directions for this field, including topics relating to platform attributes, social power, organisational culture, and employee health. [ABSTRACT FROM AUTHOR]

In this paper, a new rumor spreading model in social networks has been investigated. We propose a new version primarily based on the cholera model in order to take into account the expert pages specialized in the dissemination of rumors from an existing IRCSS model. In the second part, we recommend an optimal control strategy to fight against the spread of the rumor, and the study aims at characterizing the three optimal controls which minimize the number of spreader users, fake pages, and corresponding costs; theoretically, we have proved the existence of optimal controls, and we have given a characterization of controls in terms of states and adjoint functions based on a discrete version of Pontryagin's maximum principle. To illustrate the theoretical results obtained, we propose numerical simulations for several scenarios applying the forward-backward sweep method (FBSM) to solve our optimality system in an iterative process. [ABSTRACT FROM AUTHOR]

In this paper, a new rumor spreading model in social networks has been investigated. We propose a new version primarily based on the cholera model in order to take into account the expert pages specialized in the dissemination of rumors from an existing IRCSS model. In the second part, we recommend an optimal control strategy to fight against the spread of the rumor, and the study aims at characterizing the three optimal controls which minimize the number of spreader users, fake pages, and corresponding costs; theoretically, we have proved the existence of optimal controls, and we have given a characterization of controls in terms of states and adjoint functions based on a discrete version of Pontryagin's maximum principle. To illustrate the theoretical results obtained, we propose numerical simulations for several scenarios applying the forward-backward sweep method (FBSM) to solve our optimality system in an iterative process. [ABSTRACT FROM AUTHOR]

Influence maximization is the problem of finding a small set of nodes that maximizes the aggregated influence in social networks. The problem of influence maximization in social networks has been explored in many previous researches. They have mainly relied on similar temporal chances for every node to influence another; whereas in reality, time plays a major role in pairwise propagation rates in social networks. However, there is little research done on influence maximization considering temporal dynamics of the networks and existing approaches merely offers a mediocre performance due to ignoring trust aspects of the diffusion process. In this paper, we propose a Trust based Latency aware Influence Maximization model, abbreviated as TLIM, which selects the most influential nodes in social networks with considering time and trust simultaneously. To the best of our knowledge, we are the first to study trust in classic IC model and also the first to consider both important time and trust factors jointly in influence maximization problem. The main contributions of this paper are listed as follows: first, we extend the classic IC model to include time and trust simultaneously, which is more applicable in existing social networks. Second, we find the most influential nodes in social networks with considering time and trust together; and the last but not the least, it is applicable to well-known real social networks such as Epinions, Slashdot and Wikipedia. To explore the advantages of our approach, quite a lot of experiments with different specifications are conducted. The obtained results are very promising. [ABSTRACT FROM AUTHOR]

This paper considers an extension of the actional model of influence in online social networks. Within the framework of this model, the influence and influence levels of separate agents (users) and meta-agents (subsets of users) are calculated on the basis of their actions taking into account the goals of a control subject (a Principal). We study some properties of the influence function. An example illustrates how the actional model can be used to calculate the influence levels of users in a concrete social network under available initial data. [ABSTRACT FROM AUTHOR]

Identification and ranking of influential users in social networks for the sake of news spreading and advertising has recently become an attractive field of research. Given the large number of users in social networks and also the various relations that exist among them, providing an effective method to identify influential users has been gradually considered as an essential factor. In most of the already-provided methods, those users who are located in an appropriate structural position of the network are regarded as influential users. These methods do not usually pay attention to the interactions among users, and also consider those relations as being binary in nature. This paper, therefore, proposes a new method to identify influential users in a social network by considering those interactions that exist among the users. Since users tend to act within the frame of communities, the network is initially divided into different communities. Then the amount of interaction among users is used as a parameter to set the weight of relations existing within the network. Afterward, by determining the neighbors’ role for each user, a two-level method is proposed for both detecting users’ influence and also ranking them. Simulation and experimental results on twitter data shows that those users who are selected by the proposed method, comparing to other existing ones, are distributed in a more appropriate distance. Moreover, the proposed method outperforms the other ones in terms of both the influential speed and capacity of the users it selects. [ABSTRACT FROM AUTHOR]

Online social networks have a strong potential to be divided into a number of dense substructures, called communities. In such heterogeneous networks, the communities refer not only to dense parts of links but also to clusters present among other dimensions such as users' profiles, comments, and information flows. To find communities in these networks, researchers have developed a number of methods; however, to the best of the authors' knowledge, these methods are limited in taking only 2 dimensions into account, and they are also not able to give a sense of how users behave in their communities. To deal with these issues, this paper proposes a multiobjective optimization model in which a specific objective function has been used for each considered dimension in a given network. Because of the NP-hardness of the studied problem, an efficient and effective multiobjective metaheuristic algorithm has been developed. By juxtaposing the nondominated solutions obtained, the proposed algorithm can demonstrate how users behave in their communities. To illustrate the effectiveness of the algorithm, a set of experiments with a comprehensive evaluation method is provided. The results show the superiority and the stability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

GeoSocial Networks (GSNs) are becoming increasingly popular due to its power in providing high-performance and flexible service capabilities. More and more Internet users have accepted this innovative service model. However, even GSNs have great business value for data analysis by integrated with location information, it may seriously compromise users' privacy in publishing the GSN data. In this paper, we study the identity disclosure problem in publishing GSN data. We first discuss the attack problem by considering both the location-based and structure-based properties, as background knowledge, and then formalize two general models, named ( k , m ) - anonymity and ( k , m , l ) - anonymity . Then we propose a complete solution to achieve ( k , m ) - anonymization and ( k , m , l ) - anonymization to prevent the released data from the above attacks above. We also take data utility into consideration by defining specific information loss metrics. It is validated by real-world data that the proposed methods can prevent GSN dataset from the attacks while retaining good utility. [ABSTRACT FROM AUTHOR]

Social network is an abstract presentation of social systems where ideas and information propagate through the interactions between individuals. It is an essential issue to find a set of most influential individuals in a social network so that they can spread influence to the largest range on the network. Traditional methods for identifying influential nodes in networks are based on greedy algorithm or specific centrality measures. Some recent researches have shown that community structure, which is a common and important topological property of social networks, has significant effect on the dynamics of networks. However, most influence maximization methods do not take into consideration the community structure in the network, which limits their applications on social networks with community structure. In this paper, we propose a new algorithm for identifying influential nodes in social networks with community structure based on label propagation. The proposed algorithm can find the core nodes of different communities in the network through the label propagation process. Moreover, our algorithm has low time complexity, which makes it applicable to large-scale networks. Extensive experiments on both synthetic and real-world networks under common diffusion models demonstrate the effectiveness and efficiency of our proposed algorithm. [ABSTRACT FROM AUTHOR]

Influence maximization is a problem of identifying a small set of highly influential individuals such that obtaining the maximum value of influence spread in social networks. How to evaluate the influence is essential to solve the influence maximization problem. Meanwhile, finding out influence propagation paths is one of key factors in the assessment of influence spread. However, since nodes' degrees are utilized by most of existent models and algorithms to estimate the activation probabilities on edges, node features are always ignored in the evaluation of influence ability for different users. In this paper, besides the node features, the Credit Distribution (CD) model is extended to incorporate the time-critical aspect of influence in online social networks. After assigning credit along with the action propagation paths, we pick up the node which has maximal marginal gain in each iteration to form the seed set. The experiments we performed on real datasets demonstrate that our approach is efficient and reasonable for identifying seed nodes, and the influence spread prediction by our approach is more accurate than that of original method which disregards node features in the influence evaluation and diffusion process. [ABSTRACT FROM AUTHOR]

The propagation of memes on online social networks often depends on the mechanism of social affirmation. Centola termed such social-affirmation-driven diffusion as complex contagion and partly validated it through an online experiment. However, for actual online meme propagation, the mechanism of social affirmation often takes effect in combination with various other factors and mechanisms. In this paper, we examine the combinatorial effects of social affirmation and the attractiveness and persistence of the meme by proposing and analyzing a UACI model, where an agent's activities to receive and transfer a meme is associated with the transition between its four possible states of 'Uninformed', 'Attended','Convinced' and 'Immune'. The numerical simulations illustrate nontrivial patterns of propagation. Especially, it is revealed that the effects of simple and complex contagions co-exist and equilibrate in accordance with the joint functions of meme attractiveness and social affirmation. Furthermore, the low-persistence of the meme hinders the propagation-scale more remarkably on the regular network than on the random one, indicating that the persistence may be critical for retaining complex contagion. [ABSTRACT FROM AUTHOR]

Due to the popular use of online social networks in today’s world, how to propagate employees’ tacit knowledge via online social networks has attracted managers’ attention, which is critical to enhance the competitiveness of firms. In this paper, we propose a tacit knowledge transmission model on networks with even mixing based on the propagation property of tacit knowledge and the application of online social networks. We consider two routes of transmission, which are contact through online social networks and face-to-face physical contact, and derive the threshold that governs whether or not a kind of tacit knowledge can be shared in an organization with few initial employees who have acquired it. The impact of the degree distribution of the users’ contact network on the transmission is investigated analytically. Some numerical simulations are presented to support the theoretical results. We perform the sensitivity analysis of the threshold in terms of the propagation parameters and confirm that online social networks contribute significantly to enhancing the transmission of tacit knowledge among employees. [ABSTRACT FROM AUTHOR]

With the development of Internet and wireless communication, virtual social networks are becoming increasingly important in the formation of nowadays’ social communities. Topological evolution model is foundational and critical for social network related researches. Up to present most of the related research experiments are carried out on artificial networks, however, a study of incorporating the actual social activities into the network topology model is ignored. This paper first formalizes two mathematical abstract concepts of hobbies search and friend recommendation to model the social actions people exhibit. Then a social activities based topology evolution simulation model is developed to satisfy some well-known properties that have been discovered in real-world social networks. Empirical results show that the proposed topology evolution model has embraced several key network topological properties of concern, which can be envisioned as signatures of real social networks. [ABSTRACT FROM AUTHOR]

Abstract: Graphs are universal modeling tools. They are used to represent objects and their relationships in almost all domains: they are used to represent DNA, images, videos, social networks, XML documents, etc. When objects are represented by graphs, the problem of their comparison is a problem of comparing graphs. Comparing objects is a key task in our daily life. It is the core of a search engine, the backbone of a mining tool, etc. Nowadays, comparing objects faces the challenge of the large amount of data that this task must deal with. Moreover, when graphs are used to model these objects, it is known that graph comparison is very complex and computationally hard especially for large graphs. So, research on simplifying graph comparison gainedan interest and several solutions are proposed. In this paper, we explore and evaluate a new solution for the comparison of large graphs. Our approach relies on a compact encoding of graphs called prime graphs. Prime graphs are smaller and simpler than the original ones but they retain the structure and properties of the encoded graphs. We propose to approximate the similarity between two graphs by comparing the corresponding prime graphs. Simulations results show that this approach is effective for large graphs. [Copyright &y& Elsevier]