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Investigating online social behaviors may help us to better understand and predict offline high risk behaviors in gay communities. But how can offline behaviors be predicted from online social networks? This article selects data from 26 online social network groups from QQ (a Chinese based messaging software) administered by gay communities of "W"...
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Background
China’s population is quickly aging and this trend is expected to continue. Thus it is important to develop HIV interventions to help protect older Chinese from infection. Limited information exists regarding sexual risk behaviors and associated personal motivations among persons aged 50 and over in China.
Methods
In-depth interviews we...
The purpose of the present study was to examine differences in lifestyle factors such as physical activity among homosexual (gay or lesbian), bisexual, and heterosexual Korean adolescents.
The sample consisted of 74,186 adolescents from grades 7-12 (ages 12-18) who participated in the 8(th) annual Korea Youth Risk Behavior Web-based Survey in 2012....
Citations
... Despite the many well-documented benefits of social media, research suggests that contemporary social media exposes SNS users to the consequences of disclosure about other users caused by a spectrum of relatively unrestricted third-party actors, including adversaries, strangers, friends, and relatives (Dai, 2012). For example, adversaries who perpetuate malicious disclosure about others may target SNS users by employing unethical and illegal practices such as theft, hacking, distortion, and fabrication (Christofides, Muise, & Desmarais, 2010), practices that can damage others' finances, reputations, relationships, and employability (Rosenblum, 2007). ...
Massive amounts of information about others are disclosed across the world on social network sites every day. Disclosure of information about others may precipitate unanticipated consequences such as violating the privacy of another individual. Previous social network site (SNS) research has examined self-disclosure, this research examines the disclosure of information about others (DIO), building upon the theory of planned behavior and through the lens of Rest’s ethical decision making model. A person’s concern about others’ privacy (COP) when he/she wants to share information about them is different from concern for himself/herself.
... These neighboring vertices typically represent the most important vertices to a vertex with regard to their structural relationship in a graph. Thus, k-hop windows provide meaningful specifications for many applications, such as customer behavior analysis [3,11] , digital marketing [14] etc. ...
In relational DBMS, window functions have been widely used to facilitate data
analytics. Surprisingly, while similar concepts have been employed for graph
analytics, there has been no explicit notions of graph window analytic
functions. In this paper, we formally introduce window queries for graph
analytics. In such queries, for each vertex, the analysis is performed on a
window of vertices defined based on the graph structure. In particular, we
identify two instantiations, namely the k-hop window and the topological
window. We develop two novel indices, Dense Block index (DBIndex) and
Inheritance index (I-Index), to facilitate efficient processing of these two
types of windows respectively. Extensive experiments are conducted over both
real and synthetic datasets with hundreds of millions of vertices and edges.
Experimental results indicate that our proposed index-based query processing
solutions achieve four orders of magnitude of query performance gain than the
non-index algorithm and are superior over EAGR wrt scalability and efficiency.
Information networks are often modeled as graphs, where the vertices are associated with attributes. In this paper, we study neighborhood window analytics, namely k-hop window query, that aims to capture the properties of a local community involving the k-hop neighbors (defined on the graph structures) of each vertex. We develop a novel index, Dense Block Index (DBIndex), to facilitate efficient processing of k-hop window queries. Extensive experimental studies conducted over both real and synthetic datasets with hundreds of millions of vertices and edges show that our proposed solutions are four orders of magnitude faster in query performance than the non-index algorithm, and are superior over the state-of-the-art solution in terms of both scalability and efficiency.
