Different types of contact, including contact between node pairs, any-contact of nodes, and contacts of the entire Network, are used to characterize Social relations in mobile Social Networks. Different modes of routing, from the point of view of message delivery semantics, encompass unicasting, multicasting, any-casting, and broadcasting. Studies have shown that using probability distribution functions of contact data, which is mainly assumed to be homogeneous for nodes, improves the performance of these Networks. However, there exists an important challenge in studies on distributions. A lot of works apply the distribution of one type of contact to other types. Hence in routing applications, it causes to use of the distribution of one type of contact for any mode of routing. This study provides a complete solution to model each type of homogeneous contact data distribution and to use them in different modes of routing. We propose a routing algorithm that uses this new model. Results show that our solution improves the average latency of comparing methods Epidemic, TCCB, and DR about 3.5-times, 30%, and 45%, respectively. It achieves a delivery rate of about 5% and 6%, and average latency about 6% and 8% better than that of DR and TCCB, respectively.

Social Network monitoring (SNM) can play a significant role in everyone’ s life. Recent studies show the importance and increasing interests in the subject by Modeling and monitoring the communications between Network members over time by treating the collected observations as longitudinal data. Typically, the tendency for Modeling Social Networks, considering the dependency of an outcome variable on the covariates, is growing recently. However, these studies fail to incorporate the possible correlation between responses in the proposed models. In this paper, we use generalized linear mixed models (GLMMs), also referred to as random effects models, to model a Social Network according to the attributes of nodes in which the nodes take a role of random effect or hidden effect in Modeling. In order to estimate the regression parameters, Monte Carlo expectation maximization (MCEM) algorithm is used to maximize the likelihood function. In our simulation studies, we applied root mean square error (RMSE) and standard deviation criteria to select an appropriate model for the simulated data. Results indicate zero inflated Poisson mixed as an appropriate model for the data. In addition, compared to the other studies, our simulation study demonstrates an improvement in the average run length (ARL).

Introduction: Patient-centered care is one of the six major aims to achieve high quality healthcare. Personal health records can improve patient-centered cares. Also, there is growing desire to use internet and its services in healthcare. The purpose of this study was to design the architecture of personal health record system in the health Social Network platform.Methods: In this descriptive study, designing the architecture of the proposed system was done by Unified Modeling Language (UML). First, system components and their relationships were modeled by class diagram. Then, the use-case diagram showed how the proposed system interacts with its environment. Ultimately, the activities in the main process of the proposed system were modeled by the activity diagram. Finally, the designed architecture was evaluated by scenario-based assessment (ARID) method.Results: The scenario-based assessment (ARID) showed that the designed architecture by UML and determined relationships between components, systems and users from the structural and behavioral perspective could meet all expected scenarios from the proposed system.Conclusion: The Modeling of the proposed system showed that a Social Network can meet the features of personal health record and the expectations of its users. The use of web 2.0 and its services lead to better and faster information exchange and dissemination, training, emotional support and etc.

Background and Aim: Social Networks that provide users with health data not only educate them but also play an active role in the health decision-making process. Health Social Networks, in addition to being a good tool for better patient communication with health care providers, can play an effective role in connecting similar patients with each other to receive Social support. Social Networking is one of the biggest achievements of Web 2, which facilitates communication between people. Despite the spread of Social Networks, their use in the field of health is still at its early levels. To implement an information system, it is first necessary to identify, design and model the related processes. The main purpose of this study was to provide technical documentation for the development of Social Networks in the field of health in order to facilitate future developments. Materials and Methods: This study was an applied research. Due to the review of texts in the first phase, this research was descriptive. It is also a developmental research due to its technological dimensions in Modeling and pattern model presentation. First, extracted features were confirmed based on experts’ opinions. Then, according to the identified features, Social Network Modeling was performed at three levels of data, functional and process. Based on the Modeling, a prototype model was designed and evaluated. Results: In this research, technical documents were prepared for the development of Social Networks in the field of health in the three axes of data Modeling, functional Modeling and process Modeling. In the usability assessment by Nielsen model, the created prototype based on Modeling was evaluated. Finally, the number of problems in each case of the Nielsen model was determined. The case of "Visibility of system status" with 26. 31 and "Consistency and standards" with 5. 27 were associated with the highest and lowest problems, respectively. Conclusion: The growing need and expansion of the use of Social Networks has created a good platform for using this tool in the field of health and exploiting its benefits. The present study focuses on providing technical documentation for the development of health Social Networks and to facilitate the development of Social Networks in the field of health.

In recent years, privacy concerns about Social Network graph data publishing has increased due to the widespread use of such data for research purposes. This paper addresses the problem of identity disclosure risk of a node assuming that the adversary identifies one of its immediate neighbors in the published data. The related anonymity level of a graph is formulated and a mathematical model is proposed to solve the problem. The application of the method on a number of synthetic and real-world datasets confirms that the method is general and can be used in different contexts to produce superior results in terms of the utility of the anonymized graph.

The ethical actions are important because of the priority of the mission goal and their close relationship with Social progress and development. Therefore, the purpose of this research is the identification and Social analysis of these types of actions among the verses of revelation and it deals with the effect of ethical actions in the production of Social capital in the process of progress based on religious teachings and answers these questions based on the Quranic teachings. What impact do ethical actions (behavior) have on the formation of Social progress? How do moral actions generate Social capital and influence progress (development)? What coordinates, examples and dimensions does this group of actions have? What are the effects and consequences of this group of actions? Those actions that are desirable and progressive in the development of Social interactions have been Socially studied, analyzed and searched by using the inductive content analysis method. Honesty, trustworthiness and fulfillment of an obligation, altruism (Arabic: إیثار, self-sacrifice), asking forgiveness or istighfār (Arabic: استغفار ʾistiġfār), and forgiving others, kindness, non-violence, chastity and purity, patience and honesty and benevolent actions combined with Social supervision have been discussed, which lead to sincerity, togetherness, coherence and expansion of Social interactions and emotional relationships far from hatred and warm and emotional relationships and as a result, it causes the production of Social capital and its improvement and will lead to the density of actions focused on purpose and excellence that are necessary for progress.

In the dynamic retail service industry, enhancing productivity not only improves organizational performance but also plays a crucial role in fulfilling Social responsibility. This study models and analyzes the Network of service productivity components and ranks selected units of Etka chain stores using a hybrid Fuzzy DANP and Fuzzy WASPAS approach. Seventeen key components were identified across three dimensions—employee support, service process, and external service quality—based on the literature, with data collected through a fuzzy pairwise comparison questionnaire. First, causal relationships among components were analyzed, and final weights determined using the Fuzzy DANP method. Key drivers identified include authentic leadership, empowering organizational culture, and customer feedback, while managerial assessment and planning and systemic service design emerged as primary improvement targets. In the second step, six selected stores were ranked using Fuzzy WASPAS based on extracted weights. Results showed significant differences in productivity, with stores having data-driven feedback systems and cohesive customer interactions ranking higher. This causal-fuzzy model provides managers with an effective tool to design targeted programs for enhancing productivity, improving service quality, and strengthening organizational Social responsibility. The study’s main innovation lies in applying a hybrid Fuzzy DANP and WASPAS approach, enabling simultaneous identification of driver and consequence factors and offering a practical pathway for productivity improvement in turbulent service environments. Moreover, fuzzy Modeling bridges the gap between theory and practice, providing a precise and flexible decision-making tool for managers.