III: Small: Discovering and Characterizing Implicit Links in Graph Data

III：小：发现和表征图数据中的隐式链接

• 批准号: 1614576
• 负责人: Huan Liu
• 金额: $ 49.51万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-10-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1614576&HistoricalAwards=false
• 关键词: III; Small; Discovering; Characterizing; Implicit

Graph data represent a variety of phenomena, ranging from traffic data, biological networks to social networks. Social networks enable people to participate in a variety of online activities. A typical social networking site allows users to explicitly specify "positive" links to other users with "labels" such as family, friendships, Twitter follower, etc. Little attention is paid to "implicit links" which are unspecified links among social users that may indicate competition, distrust, dislike, or antagonism. This project studies fundamental data analytics issues of understanding and identifying implicit links in graph data. The project explores new computational techniques to discover actionable and insightful patterns in large-scale graph data (e.g., social networks) and enables a large-scale study of social media user behaviors in computational social science. The research insights gained through this project are expected to lead to better design of supervised and unsupervised learning algorithms on networks with both positive and implicit links. The study of implicit social network links will be applicable to the design of new recommender systems, leading to improved services and user experience. The proposed research will involve graduate and undergraduate students in pursuing their theses or projects. Research topics and findings will be integrated in undergraduate and graduate education. The proposed research addresses issues of link analysis in large-scale, incomplete, and noisy networks, such as underlying social media. As implicit links between users are typically invisible on social networking sites, discovering them entails novel challenges. The research team proposes to evaluate the value of implicit links for relationship discovery and better social network understanding. The project includes development of algorithms for graph data analytics, machine learning for positive and unlabeled link discovery in heterogeneous cross-media data, and computationally efficient implicit link predictions. The team proposes to apply the research insights to improving recommendation systems design, classification of implicit user relationships, and social user clustering. The research team plans to share results of this project, including benchmark data with the research community to promote the research on implicit link discovery in social networks via the project site (http://www.public.asu.edu/~huanliu/projects/ImplicitLinks/).

图数据代表了各种现象，从流量数据、生物网络到社交网络。社交网络使人们能够参与各种在线活动。典型的社交网站允许用户明确指定与其他用户的“正面”链接，并贴上“标签”，例如家人、朋友、Twitter 关注者等。很少有人关注“隐性链接”，即社交用户之间未指定的链接，这些链接可能会影响社交网站的正常运行。表示竞争、不信任、厌恶或对抗。该项目研究理解和识别图形数据中隐式链接的基本数据分析问题。该项目探索新的计算技术，以发现大规模图数据（例如社交网络）中可操作且富有洞察力的模式，并能够在计算社会科学中对社交媒体用户行为进行大规模研究。通过该项目获得的研究见解预计将有助于在具有正向和隐式链接的网络上更好地设计监督和无监督学习算法。隐式社交网络链接的研究将适用于新推荐系统的设计，从而改善服务和用户体验。拟议的研究将让研究生和本科生参与他们的论文或项目。研究课题和结果将纳入本科生和研究生教育。拟议的研究解决了大规模、不完整和嘈杂网络（例如底层社交媒体）中的链接分析问题。由于用户之间的隐式链接通常在社交网站上不可见，因此发现它们会带来新的挑战。研究团队建议评估隐式链接对于关系发现和更好的社交网络理解的价值。该项目包括开发图数据分析算法、用于异构跨媒体数据中积极和未标记链接发现的机器学习，以及计算高效的隐式链接预测。该团队建议将研究见解应用于改进推荐系统设计、隐式用户关系分类和社交用户聚类。研究团队计划通过项目网站（http://www.public.asu.edu/~huanliu/projects）与研究界分享该项目的成果，包括基准数据，以促进社交网络中隐式链接发现的研究/隐式链接/）。