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大型人际传播网络：模式和效用驱动的发电机
Large Human Communication Networks: Patterns and a Utility-Driven Generator |
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| 课程网址: |
http://videolectures.net/kdd09_du_lhcnpudg/
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| 主讲教师: |
Nan Du
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| 开课单位: |
乔治亚理工学院
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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: |
考虑到一个真实的、加权的人对人的网络, 随着时间的推移而变化, 我们能对它所包含的集团说些什么呢？沟通事件, 或集团边缘的权重, 是否遵循任何模式？真实的、亲临现场的社交网络有比偶然决定的更多的三角形。事实证明, 以令人惊讶的模式, 比人们想象的要多很多。 本文研究了通过电话、短信、im 等各种个人通信服务, 通过人与人之间的直接接触形成的大规模现实世界的社交网络。它们的贡献如下: (a) 我们发现了与集团惊人的模式, (b) 我们报告了集团边缘权重的功率定律, (c) 我们真正的网络遵循这些模式, 以便我们可以信任它们发现异常值, 最后, (d) 我们针对加权时变网络, 提出了第一个与观测到的模式相匹配的效用驱动图形生成器。我们的研究集中在三个大型数据集上, 每个数据集都是不同类型的通信服务, 有超过100万条记录, 为期几个月的活动。
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| 课程简介: |
Given a real, and weighted person-to-person network which changes over time, what can we say about the cliques that it contains? Do the incidents of communication, or weights on the edges of a clique follow any pattern? Real, and in-person social networks have many more triangles than chance would dictate. As it turns out, there are many more cliques than one would expect, in surprising patterns. In this paper, we study massive real-world social networks formed by direct contacts among people through various personal communication services, such as Phone-Call, SMS, IM etc. The contributions are the following: (a) we discover surprising patterns with the cliques, (b) we report power-laws of the weights on the edges of cliques, (c) our real networks follow these patterns such that we can trust them to spot outliers and finally, (d) we propose the first utility-driven graph generator for weighted time-evolving networks, which match the observed patterns. Our study focused on three large datasets, each of which is a different type of communication service, with over one million records, and spans several months of activity.
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| 关 键 词: |
加权网络; Web挖掘; IM; 数据集
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-06-03：张荧(课程编辑志愿者)
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| 阅读次数: |
44
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