Emergence of scale-free close-knit friendship structure in online social networks

Despite the structural properties of online social networks have attracted much attention, the properties of the close-knit friendship structures remain an important question. Here, we mainly focus on how these mesoscale structures are affected by the local and global structural properties. Analyzing the data of four large-scale online social networks reveals several common structural properties. It is found that not only the local structures given by the indegree, outdegree, and reciprocal degree distributions follow a similar scaling behavior, the mesoscale structures represented by the distributions of close-knit friendship structures also exhibit a similar scaling law. The degree correlation is very weak over a wide range of the degrees. We propose a simple directed network model that captures the observed properties. The model incorporates two mechanisms: reciprocation and preferential attachment. Through rate equation analysis of our model, the local-scale and mesoscale structural properties are derived. In the local-scale, the same scaling behavior of indegree and outdegree distributions stems from indegree and outdegree of nodes both growing as the same function of the introduction time, and the reciprocal degree distribution also shows the same power-law due to the linear relationship between the reciprocal degree and in/outdegree of nodes. In the mesoscale, the distributions of four closed triples representing close-knit friendship structures are found to exhibit identical power-laws, a behavior attributed to the negligible degree correlations. Intriguingly, all the power-law exponents of the distributions in the local-scale and mesoscale depend only on one global parameter -- the mean in/outdegree, while both the mean in/outdegree and the reciprocity together determine the ratio of the reciprocal degree of a node to its in/outdegree.Comment: 48 pages, 34 figure

Lower Ricci Curvature for Efficient Community Detection

This study introduces the Lower Ricci Curvature (LRC), a novel, scalable, and scale-free discrete curvature designed to enhance community detection in networks. Addressing the computational challenges posed by existing curvature-based methods, LRC offers a streamlined approach with linear computational complexity, making it well-suited for large-scale network analysis. We further develop an LRC-based preprocessing method that effectively augments popular community detection algorithms. Through comprehensive simulations and applications on real-world datasets, including the NCAA football league network, the DBLP collaboration network, the Amazon product co-purchasing network, and the YouTube social network, we demonstrate the efficacy of our method in significantly improving the performance of various community detection algorithms

How consumers perceive vloggers? Exploring consumer’s perceptions and purchase intention applied to beauty and fashion industry

Con la evolución de los medios digitales y, en consecuencia, con la aparición de nuevas plataformas, los consumidores son más receptivos al contenido compartido en las redes sociales, como es el caso de YouTube, en busca de información para ayudarles en su toma de decisiones. De esta manera, la industria de la moda y la belleza es darse cuenta de la importancia de invertir en campañas de publicidad y de comunicación digitales y en este canal a través de influenciadores digitales. Por lo tanto, la investigación se centra en la red social YouTube y vloggers, aplicado en Portugal y España. Se persigue el objetivo de comprender la dinámica entre las percepciones de los consumidores sobre el contenido de belleza y moda vídeos y la intención de compra, así como entre la credibilidad de percionada vlogger y intención de compra.With the evolution of the digital age and consequently with the emergence of new platforms, consumers are more receptive to the shared content on social networks, for instance with YouTube, looking for information that helps them in their decision making. In this way, the fashion and beauty industry is realizing the importance of digital media and investing in communication and advertising campaigns with digital influencers. Therefore, the research focuses on the social network YouTube and vloggers, applied in Portugal and Spain. It is intended to understand the dynamics between the perceptions of consumers to the content of fashion and beauty videos and the intention of purchase as well as between the perceived credibility of vlogger and the intention of purchase

YOUTUBE SOCIAL NETWORK

Article analyzes Youtube definition, youtube advantages and disadvantages, youtube advertising. Thanks to Youtube, a larger number of users can be reached and allows you to see the reaction of the players to the video advertising. YouTube is one of the most popular sites on the world, which receives millions of impressions annually and spends millions of content makers that it has. The profit earned through YouTube comes from advertising. Content developers who post ads in their videos receive about half of the revenue generated by these ads. Youtube is a social network that allows you to make money

The Untold Story of the Clones: Content-agnostic Factors that Impact YouTube Video Popularity

Video dissemination through sites such as YouTube can have widespread impacts on opinions, thoughts, and cultures. Not all videos will reach the same popularity and have the same impact. Popularity differences arise not only because of differences in video content, but also because of other "content-agnostic" factors. The latter factors are of considerable interest but it has been difficult to accurately study them. For example, videos uploaded by users with large social networks may tend to be more popular because they tend to have more interesting content, not because social network size has a substantial direct impact on popularity. In this paper, we develop and apply a methodology that is able to accurately assess, both qualitatively and quantitatively, the impacts of various content-agnostic factors on video popularity. When controlling for video content, we observe a strong linear "rich-get-richer" behavior, with the total number of previous views as the most important factor except for very young videos. The second most important factor is found to be video age. We analyze a number of phenomena that may contribute to rich-get-richer, including the first-mover advantage, and search bias towards popular videos. For young videos we find that factors other than the total number of previous views, such as uploader characteristics and number of keywords, become relatively more important. Our findings also confirm that inaccurate conclusions can be reached when not controlling for content.Comment: Dataset available at: http://www.ida.liu.se/~nikca/papers/kdd12.htm