1,290,540 research outputs found

    Social Network Data Management

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    With the increasing usage of online social networks and the semantic web's graph structured RDF framework, and the rising adoption of networks in various fields from biology to social science, there is a rapidly growing need for indexing, querying, and analyzing massive graph structured data. Facebook has amassed over 500 million users creating huge volumes of highly connected data. Governments have made RDF datasets containing billions of triples available to the public. In the life sciences, researches have started to connect disparate data sets of research results into one giant network of valuable information. Clearly, networks are becoming increasingly popular and growing rapidly in size, requiring scalable solutions for network data management. This thesis focuses on the following aspects of network data management. We present a hierarchical index structure for external memory storage of network data that aims to maximize data locality. We propose efficient algorithms to answer subgraph matching queries against network databases and discuss effective pruning strategies to improve performance. We show how adaptive cost models can speed up subgraph matching query answering by assigning budgets to index retrieval operations and adjusting the query plan while executing. We develop a cloud oriented social network database, COSI, which handles massive network datasets too large for a single computer by partitioning the data across multiple machines and achieving high performance query answering through asynchronous parallelization and cluster-aware heuristics. Tracking multiple standing queries against a social network database is much faster with our novel multi-view maintenance algorithm, which exploits common substructures between queries. To capture uncertainty inherent in social network querying, we define probabilistic subgraph matching queries over deterministic graph data and propose algorithms to answer them efficiently. Finally, we introduce a general relational machine learning framework and rule-based language, Probabilistic Soft Logic, to learn from and probabilistically reason about social network data and describe applications to information integration and information fusion

    Towards Research Collaboration – a Taxonomy of Social Research Network Sites

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    The increase of scientific collaboration coincides with the technological and social advancement of social software applications which can change the way we research. Among social software, social network sites have recently gained immense popularity in a hedonic context. This paper focuses on social network sites as an emerging application designed for the specific needs of researchers. To give an overview about these sites we use a data set of 24 case studies and in-depth interviews with the founders of ten social research network sites. The gathered data leads to a first tentative taxonomy and to a definition of SRNS identifying four basic functionalities identity and network management, communication, information management, and collaboration. The sites in the sample correspond to one of the following four types: research directory sites, research awareness sites, research management sites and research collaboration sites. These results conclude with implications for providers of social research network sites

    Why Youth (heart) Social Network Sites: The Role of Networked Publics in Teenage Social Life

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    Part of the Volume on Youth, Identity, and Digital Media Social network sites like MySpace and Facebook serve as "networked publics." As with unmediated publics like parks and malls, youth use networked publics to gather, socialize with their peers, and make sense of and help build the culture around them. This article examines American youth engagement in networked publics and considers how properties unique to such mediated environments (e.g., persistence, searchability, replicability, and invisible audiences) affect the ways in which youth interact with one another. Ethnographic data is used to analyze how youth recognize these structural properties and find innovative ways of making these systems serve their purposes. Issues like privacy and impression management are explored through the practices of teens and youth participation in social network sites is situated in a historical discussion of youth's freedom and mobility in the United States

    An Analysis of the Social Networks in Health Promoting Schools

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    This multi-case study aims to compare the social networks of health promoting schools with different qualities of health service management. Selected using apurposive sampling technique, the samples were three secondary schools in Nonthaburi Province under the Office of the Basic Education Commission. The criteria for the selection were school size and the quality of health service management. From the three sample schools, 181 informants participated in the study. Data analysis included descriptive statistics, social network analyses using UCINET and Net Draw programs. The results were as follows. The social networks in health promoting schools with better health service management were larger and less centralized. Analyses of the whole-networks revealed that schools with different qualities of health service management had different social network characteristics. By the comparison of social networks of schools, it was found that network size and centralization can be used to categorize schools with diverse good health service management. The density of the network and eigenvector centrality cannot be clearly categorized.
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