Analisis Potensi Penyebaran Informasi Kesehatan melalui Jejaring Sosial (Studi Kasus pada \u27Forum Jejaring Peduli AIDS\u27)

Background: Predicted in 2015 there was an increase of 924.000 cases with a prevalence of 0.49%, and rose sharply to 2.117 million cases in 2025 with a prevalence of 1.00%. This surge can be prevented to less than half that when the target of universal access can be achieved by 2014. For prevention, FJPA using Facebook as a medium of information delivery. This effort shows remarkable progress. This development becomes interesting to study, how the effectiveness of Facebook Social Media Networking in a process of diffusion of information related to HIV/AIDS. Methods: The method used in this analysis is content analysis. Observations conducted in 2010, March 18, 2010 until December 31, 2010. Result: The results showed a sharp increase in membership FJPA on Facebook that reach 2821 members by 2010. Membership has exceeded the limits of the country, including men and women almost equally, dominated age. FJPA page on Facebook last ten months has been accessed 4278 times, the interaction peak in the first month (March 2010) 430 interactions, and six months subsequent interaction 77-132 per month, and peaked back in December of 309. Posting an entry consisting of 126 wall posts, links 31, and 35 notes. It can be concluded that the Social Networking Media through the Internet very effectively as a medium of diffusion of information which transcends geographical and administrative regions. Social Networking is also an effective medium for dissemination of information to target youth and age. The recommendation is: Social Media Networks need more intensive process of diffusion health information, specially productive age as a target; The review also needs to be continued to look at the effectiveness of other social networking media. Such as Twiter, Koprol, Blogs, and so on

Revisiting Resolution and Inter-Layer Coupling Factors in Modularity for Multilayer Networks

Modularity for multilayer networks, also called multislice modularity, is parametric to a resolution factor and an inter-layer coupling factor. The former is useful to express layer-specific relevance and the latter quantifies the strength of node linkage across the layers of a network. However, such parameters can be set arbitrarily, thus discarding any structure information at graph or community level. Other issues are related to the inability of properly modeling order relations over the layers, which is required for dynamic networks. In this paper we propose a new definition of modularity for multilayer networks that aims to overcome major issues of existing multislice modularity. We revise the role and semantics of the layer-specific resolution and inter-layer coupling terms, and define parameter-free unsupervised approaches for their computation, by using information from the within-layer and inter-layer structures of the communities. Moreover, our formulation of multilayer modularity is general enough to account for an available ordering of the layers and relating constraints on layer coupling. Experimental evaluation was conducted using three state-of-the-art methods for multilayer community detection and nine real-world multilayer networks. Results have shown the significance of our modularity, disclosing the effects of different combinations of the resolution and inter-layer coupling functions. This work can pave the way for the development of new optimization methods for discovering community structures in multilayer networks.Comment: Accepted at the IEEE/ACM Conf. on Advances in Social Network Analysis and Mining (ASONAM 2017

Temporal Locality in Today's Content Caching: Why it Matters and How to Model it

The dimensioning of caching systems represents a difficult task in the design of infrastructures for content distribution in the current Internet. This paper addresses the problem of defining a realistic arrival process for the content requests generated by users, due its critical importance for both analytical and simulative evaluations of the performance of caching systems. First, with the aid of YouTube traces collected inside operational residential networks, we identify the characteristics of real traffic that need to be considered or can be safely neglected in order to accurately predict the performance of a cache. Second, we propose a new parsimonious traffic model, named the Shot Noise Model (SNM), that enables users to natively capture the dynamics of content popularity, whilst still being sufficiently simple to be employed effectively for both analytical and scalable simulative studies of caching systems. Finally, our results show that the SNM presents a much better solution to account for the temporal locality observed in real traffic compared to existing approaches.Comment: 7 pages, 7 figures, Accepted for publication in ACM Computer Communication Revie

Why Do People Use Social Networks?

Online social networking (OSN) sites such as Facebook, YouTube, and Twitter are among the most popular sites around the World. In the case of Mexico, these sites are considered to be in the top. Users have different tools for reading and sharing information with his friends and/or contacts and for searching for new users that might have common interests. These sites have changed the way people get connected to each other on the web. The popularity of these sites is represented on its registered users: as today Facebook has about 500 million, and Twitter about 175 million. As these numbers grow, we believe that there is a great opportunity to study critical characteristics of online social network in order to identify the key success factors based on end users perception. Identifying such factors would provide important information for improving current information systems by including social software characteristics as well as to design new high impact applications for online social networks. This paper presents the results obtained through a focus group study to identify the most important issues and perceptions about OSN. Results give us an idea where and what we should be doing for future research in the subject

From sparse to dense and from assortative to disassortative in online social networks

Inspired by the analysis of several empirical online social networks, we propose a simple reaction-diffusion-like coevolving model, in which individuals are activated to create links based on their states, influenced by local dynamics and their own intention. It is shown that the model can reproduce the remarkable properties observed in empirical online social networks; in particular, the assortative coefficients are neutral or negative, and the power law exponents are smaller than 2. Moreover, we demonstrate that, under appropriate conditions, the model network naturally makes transition(s) from assortative to disassortative, and from sparse to dense in their characteristics. The model is useful in understanding the formation and evolution of online social networks.Comment: 10 pages, 7 figures and 2 table