CIF21 DIBBs: Designing the Roadmap for Social Network Data Management

CIF21Dibbs：设计社交网络数据管理路线图在社会科学和其他领域，学术界对网络分析的兴趣急剧增加。Facebook、Flickr和Twitter等社交媒体工具的爆炸性增长，以及机器学习和数据挖掘的新发展，产生了新类型的行为数据，供学者分析(MisLove等人，2007)。数学、统计学和计算机科学的重大进步也创造了前所未有的分析机会。社交网络数据。社交网络分析处于社会科学的前沿，也将社会、自然和计算科学联系在一起。了解社交网络的多面性及其对人类行为的影响是该项目寻求最大限度地增加我们在科学研究方面的投资所面临的重大挑战之一(NSF-ACCI，2011)。然而，在社交网络数据的管理、归档和共享方面，社区还没有看到类似的进步。这是在社会科学、自然科学和计算科学领域推进网络科学的根本障碍。这项提议试图开始纠正这个问题。社交网络学者对数据管理的需求比较复杂。网络数据通常来自非结构化环境，需要研究人员定义和描述一组单元或参与者(称为节点)以及它们之间的连接(称为边)。网络可以是静态的或动态的，包括一种类型的节点或多个节点类型，并包括单向或双向的边以及加权或未加权的边。此外，随着关系在网络和/或网络中的扩展，相关的数据管理、数据存储和分析内存需求可能会呈指数级增长。这项建议将社交网络分析、信息科学、计算机科学和数据归档社区聚集在一起，以开发数据基础设施，以支持对社交网络的高级分析和研究，并促进社区内的数据共享和归档。该小组将解决有关数据存储体系结构和生命周期要求的关键问题，制定设计规范，以创建可发现、可搜索、可访问并可供整个研究和教育社区使用的可持续数据基础设施，并根据该计划启动原型解决方案。智力优势：拟议的项目将把社交网络分析社区聚集在一起，与信息技术专业人员合作，设计一个强大的数据管理基础设施，以促进社交网络数据的共享和互操作性。对社交网络数据进行有效的数据管理可在数据收集过程的早期揭示数据质量问题，从而扩大研究的影响，确保所需数据在研究项目的整个生命周期中得到保留和使用。它还促进了数据共享和重复使用。负责任的数据管理的一个关键是应用和审核质量数据管理策略？这项提案中包含的某些内容。提供强大的基础设施来存储、分析、管理、共享和管理重要的社交网络数据是否会增加研究人员？生产，并提供一个前所未有的视角的社会世界，只有通过社交网络数据可见。更广泛的影响：该项目将促进数据共享，增加社交网络数据的可用性，同时向研究人员保证数据管理政策得到遵守。它将有助于将网络数据专家社区正规化，开始为社区制定最佳实践。数据的可获得性尤其有利于早期研究人员和不同机构的研究人员。数据的广泛可获得性促进了公民科学以及各级教育中科学与教学的融合。管理数据共享对于多学科研究以应对当今社会面临的关键挑战至关重要。要更好地理解人际网络和社交行为，社交网络分析的重要性怎么强调都不为过。

CIF21 DIBBs: Designing the Roadmap for Social Network Data Management Scholarly interest in network analysis has increased dramatically in the social sciences and beyond. The explosion of social media tools such as Facebook, Flickr, and Twitter, along with new developments in machine learning and data mining have produced new types of behavioral data for scholars to analyze (Mislove et al., 2007). Significant advances in mathematics, statistics, and computer science have also produced unprecedented opportunities to analyze ?Big? social network data. Social network analysis sits at the cutting edge of social science and also links the social, natural, and computational sciences. Understanding the multi-faceted nature of social networks and their effects on human behavior is one of the grand challenges faced as this project seeks to maximize our investments in scientific research (NSF-ACCI, 2011). However, the community has not seen comparable advances in the management, archiving, and sharing of social network data. This presents a fundamental obstacle to advancing network science across the social, natural, and computational sciences. This proposal seeks to begin to remedy this problem. The data management needs social network scholars are complex. Network data often come from unstructured environments that require researchers to define and describe a set of units or actors (called nodes) and the connections (called edges) between them. Networks might be static or dynamic, include one type of node or multiple node types, and include edges that are uni-directional or bi-directional and weighted or unweighted. In addition, as relationships spread within the network and/or a network grows, the associated data management, data storage, and analytical memory requirements can grow exponentially. This proposal brings together the social network analysis, information science, computer science, and data archive communities to develop a data infrastructure to support advanced analysis and research on social networks as well as to facilitate data sharing and archiving within this community. The group will address key questions concerning data storage architecture and lifecycle requirements, develop design specifications for creating a sustainable data infrastructure that will be discoverable, searchable, accessible, and usable to the entire research and education community, and initialize a prototype solution based on that plan. Intellectual Merit: The proposed project will bring together the social network analysis community to work with information technology professionals to design a robust data management infrastructure to promote the sharing and interoperability of social network data. Effective data management for social network data amplifies the impact of research by revealing data quality issues early in the data collection process, ensuring that required data is retained and usable throughout the life of a research project. It also facilitates data sharing and reuse. A key to responsible data stewardship is the application and auditing of quality data management policies ? something included in this proposal. Providing a robust infrastructure to store, analyze, curate, share, and manage important social network data will increase researchers? production and provide an unprecedented view of the social world only visible through social network data. Broader Impacts: The project will facilitate data sharing and increase social network data availability while assuring researchers that data management policies are followed. It will help formalize a community of network data experts that will begin developing best practices for the community. Availability of data particularly benefits early-stage researchers, and researchers at diverse institutions. Widespread availability of data facilitates citizen science and the integration of science and teaching at all levels of education. Managed data sharing is critical to the multi disciplinary research to answer the critical challenges facing society today. It would be difficult to overstate the importance of social network analysis to better understand human networks and social behavior.