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MetaFac:社区发现需通过关联超图的因子分解
MetaFac: Community Discovery via Relational Hypergraph Factorization |
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| 课程网址: |
http://videolectures.net/kdd09_lin_mfcdvrh/
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| 主讲教师: |
Yu-Ru Lin
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| 开课单位: |
匹兹堡大学
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| 开课时间: |
2009-09-14
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| 课程语种: |
英语
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| 中文简介: |
本文旨在通过对时变、多关系数据的分析，发现富媒体社交网络中的社区结构。社区结构代表用户行为的潜在社会背景。它在搜索和推荐等信息任务中有重要的应用。社交媒体有几个独特的挑战。（a）在社交媒体中，用户行为的语境是不断变化和共同演变的，因此，社交语境包含着时间演进的多维关系。（b）社交环境由可用的系统功能决定，并且在每个社交媒体网站中都是唯一的。在本文中，我们提出了元图分解（metafac），一种从各种社会背景和交互中提取社区结构的框架。我们的工作有三个关键贡献：（1）元图（metagraph），一种新的用于建立多关系和多维社会数据模型的关系超图表示；（2）一种有效的基于给定元图的社区提取因子分解方法；（3）一种通过增量元图因子分解处理时变关系的在线方法。从Digg社交媒体网站收集的大量现实社会数据实验表明，我们的技术具有可扩展性，能够根据社交媒体环境提取有意义的社区。我们通过预测任务来说明框架的有用性。我们在数量级上优于基线方法（包括方面模型和张量分析）。
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| 课程简介: |
This paper aims at discovering community structure in rich media social networks, through analysis of time-varying, multi-relational data. Community structure represents the latent social context of user actions. It has important applications in information tasks such as search and recommendation. Social media has several unique challenges. (a) In social media, the context of user actions is constantly changing and co-evolving; hence the social context contains time-evolving multi-dimensional relations. (b) The social context is determined by the available system features and is unique in each social media website. In this paper we propose MetaFac (MetaGraph Factorization), a framework that extracts community structures from various social contexts and interactions. Our work has three key contributions: (1) metagraph, a novel relational hypergraph representation for modeling multi-relational and multi-dimensional social data; (2) an efficient factorization method for community extraction on a given metagraph; (3) an on-line method to handle time-varying relations through incremental metagraph factorization. Extensive experiments on real-world social data collected from the Digg social media website suggest that our technique is scalable and is able to extract meaningful communities based on the social media contexts. We illustrate the usefulness of our framework through prediction tasks. We outperform baseline methods (including aspect model and tensor analysis) by an order of magnitude.
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| 关 键 词: |
多关系数据; 社交网络; 信息检索; 社会关联超图; 元图分解
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-12-21：lxf
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| 阅读次数: |
67
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