Networks, Urban

For much of the twentieth century, urban networks was a term used by sociologists and others to describe social networks, their importance for bonding within communities and bridging between communities, and their relationship to the geographical mobility implied by late-nineteenth- and early-twentieth-century urbanization, mid-twentieth-century suburbanization, and late-twentieth-century globalization. This relationship is often assumed to be one in which social networks are threatened by geographical mobility. From sometime in the 1980s, in a context of globalization, network became a metaphor used across the social sciences to describe how people, ideas, and objects flow between nodes in a globalizing world, and urban networks became a term used by geographers and others to describe at least four more or less connected things: (1) archipelagos of world or global cities, in which centrality depends on networks of producer services and information and communications technology infrastructure; (2) this information and communications technology infrastructure, among other networked infrastructure, which has become unbundled in recent years, leading to fragmented or splintered cities; (3) other smaller networks of humans and nonhumans – actor networks – that help to maintain urban life; and (4) twenty-first-century social networks, characterized by their transnational geographies and relatively high levels of institutionalization and self-consciousnes

Fertility-relevant social networks: composition, structure, and meaning of personal relationships for fertility intentions

Although the relevance of social interactions or social networks for fertility research has been increasingly acknowledged in recent years, little is known about the channels and mechanisms of social influences on individuals� fertility decision making. Drawing on problem-centred interviews and network data collected among young adults in western Germany the authors show that qualitative methods broaden our understanding of social and contextual influences on couples� fertility intentions, by exploring the phenomenon, taking into account subjective perceptions, analysing interactions within networks as well as the dynamics of networks. Qualitative methods allow for the collection and analysis of rich retrospective information on network dynamics in relation to life course events. This also can be helpful both to complement the still rare longitudinal data on social networks and to develop parsimonious and efficient survey instruments to collect such information in a standardized way.Germany, fertility, qualitative methods, social network

Literature Overview - Privacy in Online Social Networks

In recent years, Online Social Networks (OSNs) have become an important\ud part of daily life for many. Users build explicit networks to represent their\ud social relationships, either existing or new. Users also often upload and share a plethora of information related to their personal lives. The potential privacy risks of such behavior are often underestimated or ignored. For example, users often disclose personal information to a larger audience than intended. Users may even post information about others without their consent. A lack of experience and awareness in users, as well as proper tools and design of the OSNs, perpetuate the situation. This paper aims to provide insight into such privacy issues and looks at OSNs, their associated privacy risks, and existing research into solutions. The final goal is to help identify the research directions for the Kindred Spirits project

Analyzing the Facebook Friendship Graph

Online Social Networks (OSN) during last years acquired a\ud huge and increasing popularity as one of the most important emerging Web phenomena, deeply modifying the behavior of users and contributing to build a solid substrate of connections and relationships among people using the Web. In this preliminary work paper, our purpose is to analyze Facebook, considering a signi�cant sample of data re\ud ecting relationships among subscribed users. Our goal is to extract, from this platform, relevant information about the distribution of these relations and exploit tools and algorithms provided by the Social Network Analysis (SNA) to discover and, possibly, understand underlying similarities\ud between the developing of OSN and real-life social networks

The Tips for Social Networking Safety

The Internet is definitely not secure. Most of us have ever encountered security problems in the social networks. The most common troubles arising in the network are: the issues of confidentiality, hacking and stealing passwords and potential problems in the workplace. Some tips how to deal with these problems are given in this paper. When posting information about yourself in social media, anyone must be prepared that a large number of people all over the world can see it. Thus, your private life becomes public. Even if you take all the measures for your personal information protection from people you don’t know, these attempts, may be useless as there are many hacker programs that help to select passwords for popular websites and hack them. Moreover, people face potential problems in the workplace because of social networks use. For example, you can post information about colleagues or your boss that can be interpreted as a disclosure of confidential information, discrediting you in the eyes of the company, that may lead to serious consequences

DeepInf: Social Influence Prediction with Deep Learning

Social and information networking activities such as on Facebook, Twitter, WeChat, and Weibo have become an indispensable part of our everyday life, where we can easily access friends' behaviors and are in turn influenced by them. Consequently, an effective social influence prediction for each user is critical for a variety of applications such as online recommendation and advertising. Conventional social influence prediction approaches typically design various hand-crafted rules to extract user- and network-specific features. However, their effectiveness heavily relies on the knowledge of domain experts. As a result, it is usually difficult to generalize them into different domains. Inspired by the recent success of deep neural networks in a wide range of computing applications, we design an end-to-end framework, DeepInf, to learn users' latent feature representation for predicting social influence. In general, DeepInf takes a user's local network as the input to a graph neural network for learning her latent social representation. We design strategies to incorporate both network structures and user-specific features into convolutional neural and attention networks. Extensive experiments on Open Academic Graph, Twitter, Weibo, and Digg, representing different types of social and information networks, demonstrate that the proposed end-to-end model, DeepInf, significantly outperforms traditional feature engineering-based approaches, suggesting the effectiveness of representation learning for social applications.Comment: 10 pages, 5 figures, to appear in KDD 2018 proceeding

Location Prediction: Communities Speak Louder than Friends

Humans are social animals, they interact with different communities of friends to conduct different activities. The literature shows that human mobility is constrained by their social relations. In this paper, we investigate the social impact of a person's communities on his mobility, instead of all friends from his online social networks. This study can be particularly useful, as certain social behaviors are influenced by specific communities but not all friends. To achieve our goal, we first develop a measure to characterize a person's social diversity, which we term `community entropy'. Through analysis of two real-life datasets, we demonstrate that a person's mobility is influenced only by a small fraction of his communities and the influence depends on the social contexts of the communities. We then exploit machine learning techniques to predict users' future movement based on their communities' information. Extensive experiments demonstrate the prediction's effectiveness.Comment: ACM Conference on Online Social Networks 2015, COSN 201

Social media and student lifecycle: impact on career success

Over the past few years online social networks have become one of the most popular applications on the Internet. Naturally, social media is attracting a significant attention from researchers probing its educational applicability. Online social networking services (SNS) offer a straightforward way to connect people and support information sharing and communication. University students are often ahead of the rest in the adoption of new technologies, and according to (Quan-Haase, 2007) their communication networks tend to be dense and multilayered. Extant literature abounds with evidence of business opportunities (e.g. Aldrich & Kim, 2007) and educational use (e.g. Mastrodicasa,2008) of social networks. However, very little research attention has been paid towards a systematic adoption of SNS throughout the complete student lifecycle . With the aim of achieving higher levels of success in learning as well as improving their career prospects. This study investigates the use of social media by business students. KU business students and students from four international HE institutions in Europe, including Russia and Greece, have participated in the study. Social media has the potential of providing an easy-to use platform to connect students throughout their entire lifecycle from aspiration rising, enrolment, learning and teaching leading on to employment, alumni communication and life-long learning. This is especially important as the stages of employability management and life-long learning take a centre stage in managing student expectations and influencing their decision of taking up places at which university