Archive for the ‘Social network’ Category
In complex social systems such as those of many mammals, including humans, groups (and hence ego-centric social networks) are commonly structured in discrete layers. We describe a computational model for the development of social relationships based on agents’ strategies for social interaction that favour more less-intense, or fewer more-intense partners. A trust-related process controls the formation and decay of relationships as a function of interaction frequency, the history of interaction, and the agents’ strategies. A good fit of the observed layers of human social networks was found across a range of model parameter settings. Social interaction strategies which favour interacting with existing strong ties or a time-variant strategy produced more observation-conformant results than strategies favouring more weak relationships. Strong-tie strategies spread in populations under a range of fitness conditions favouring wellbeing, whereas weak-tie strategies spread when fitness favours foraging for food. The implications for modelling the emergence of social relationships in complex structured social networks are discussed.
Social networks and online communities are reshaping the way people communicate, both in their personal and professional lives. What makes some succeed and others fail? What draws a user in? What makes them join? What keeps them coming back? Entrepreneurs and businesses are turning to user experience practitioners to figure this out. Though they are well-equipped to evaluate and create a variety of interfaces, social networks require a different set of design principles and ways of thinking about the user in order to be successful.
Design to Thrive presents tried and tested design methodologies to ensure successful and sustainable online communities. The book describes four criteria, called “RIBS,” which are necessary to the design of a successful and sustainable online community. These concepts provide designers with the tools they need to generate informed creative and productive design ideas, to think proactively about the communities they are building or maintaining, and to design communities that encourage users to actively contribute.
In today’s flatter organizations, collaboration in employee networks has become critical to innovation and to both individual and company wide performance. Executives spend millions on new organizational designs, cultural initiatives, and technologies to promote the sharing of knowledge and expertise across functional, hierarchical, and divisional lines. Yet these efforts have achieved disappointing results.
Rob Cross and Andrew Parker argue that’s because most managers have little understanding of how their employees actually interact to get work done. In fact, formal “org charts” fail to reveal the often hidden social networks that truly drive–or hinder–an organization’s performance. In this eye-opening book, Cross and Parker show managers how to find, assess, and support the networks most crucial to competitive success.
Based on their in-depth study of more than sixty informal networks within organizations around the world, Cross and Parker show how managers can implement a wide range of specific and inexpensive actions-from bridging strategically important disconnects in a network to eliminating information “bottlenecks” to recognizing key connectors-that will enhance the powerful impact networks can have on performance and innovation.
Despite the swift spread of social network concepts and their applications and the rising use of network analysis in social science, there is no book that provides a thorough general introduction for the serious reader. Understanding Social Networks fills that gap by explaining the big ideas that underlie the social network phenomenon. Written for those interested in this fast moving area but who are not mathematically inclined, it covers fundamental concepts, then discusses networks and their core themes in increasing order of complexity. Kadushin demystifies the concepts, theories, and findings developed by network experts. He selects material that serves as basic building blocks and examples of best practices that will allow the reader to understand and evaluate new developments as they emerge. Understanding Social Networks will be useful to social scientists who encounter social network research in their reading, students new to the network field, as well as managers, marketers, and others who constantly encounter social networks in their work.
Does your start-up rely on social network analysis? This concise guide provides a statistical framework to help you identify social processes hidden among the tons of data now available.
Social network analysis (SNA) is a discipline that predates Facebook and Twitter by 30 years. Through expert SNA researchers, you’ll learn concepts and techniques for recognizing patterns in social media, political groups, companies, cultural trends, and interpersonal networks. You’ll also learn how to use Python and other open source tools—such as NetworkX, NumPy, and Matplotlib—to gather, analyze, and visualize social data. This book is the perfect marriage between social network theory and practice, and a valuable source of insight and ideas.
Daily life is connected life, its rhythms driven by endless email pings and responses, the chimes and beeps of continually arriving text messages, tweets and retweets, Facebook updates, pictures and videos to post and discuss. Our perpetual connectedness gives us endless opportunities to be part of the give-and-take of networking.
Some worry that this new environment makes us isolated and lonely. But in Networked, Lee Rainie and Barry Wellman show how the large, loosely knit social circles of networked individuals expand opportunities for learning, problem solving, decision making, and personal interaction. The new social operating system of “networked individualism” liberates us from the restrictions of tightly knit groups; it also requires us to develop networking skills and strategies, work on maintaining ties, and balance multiple overlapping networks. Rainie and Wellman outline the”triple revolution” that has brought on this transformation: the rise of social networking, the capacity of the Internet to empower individuals, and the always-on connectivity of mobile devices. Drawing on extensive evidence, they examine how the move to networked individualism has expanded personal relationships beyond households and neighborhoods; transformed work into less hierarchical, more team-driven enterprises; encouraged individuals to create and share content; and changed the way people obtain information. Rainie and Wellman guide us through the challenges and opportunities of living in the evolving world of networked individuals.
This article explores the role of ‘critical mass’ and social networks in the generation of collective action. Drawing on qualitative and quantitative (social network) data, the article argues that both are pivotal in the process whereby collective action takes shape. The empirical focus of the article is student politics but it is argued that the mechanisms and dynamics identified have a much wider domain of application.
Complex networks emerge under different conditions including design (i.e., top-down decisions) through simple rules of growth and evolution. Such rules are typically local when dealing with biological systems and most social webs. An important deviation from such a scenario is provided by groups, collectives of agents engaged in technology development, such as open-source communities. Here we analyze their network structure, showing that it defines a complex weighted network with scaling laws at different levels, as measured by looking at e-mail exchanges. We also present a simple model of network growth involving non-local rules based on betweenness centrality. Our weighted network analysis suggests that a well-defined interplay between the overall goals of the community and the underlying hierarchical organization play a key role in shaping its dynamics.
Most scholars, politicians, and activists are following individualistic theories of privacy and data protection. In contrast, some of the pioneers of the data protection legislation in Germany like Adalbert Podlech, Paul J. M\”uller, and Ulrich Dammann used a systems theory approach. Following Niklas Luhmann, the aim of data protection is (1) maintaining the functional differentiation of society against the threats posed by the possibilities of modern information processing, and (2) countering undue information power by organized social players. It could be, therefore, no surprise that the first data protection law in the German state of Hesse contained rules to protect the individual as well as the balance of power between the legislative and the executive body of the state. Social networks like Facebook or Google+ do not only endanger their users by exposing them to other users or the public. They constitute, first and foremost, a threat to society as a whole by collecting information about individuals, groups, and organizations from different social systems and combining them in a centralized data bank. They transgress the boundaries between social systems that act as a shield against total visibility and transparency of the individual and protect the freedom and the autonomy of the people. Without enforcing structural limitations on the organizational use of collected data by the social network itself or the company behind it, social networks pose the worst totalitarian peril for western societies since the fall of the Soviet Union.
In empirical studies of friendship networks participants are typically asked, in interviews or questionnaires, to identify some or all of their close friends, resulting in a directed network in which friendships can, and often do, run in only one direction between a pair of individuals. Here we analyze a large collection of such networks representing friendships among students at US high and junior-high schools and show that the pattern of unreciprocated friendships is far from random. In every network, without exception, we find that there exists a ranking of participants, from low to high, such that almost all unreciprocated friendships consist of a lower-ranked individual claiming friendship with a higher-ranked one. We present a maximum-likelihood method for deducing such rankings from observed network data and conjecture that the rankings produced reflect a measure of social status. We note in particular that reciprocated and unreciprocated friendships obey different statistics, suggesting different formation processes, and that rankings are correlated with other characteristics of the participants that are traditionally associated with status, such as age and overall popularity as measured by total number of friends.