图 书 馆
Meta path based ensemble link prediction in multiple networksMeta-path based Multi-network Collective Link Prediction |
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| 课程网址: | http://videolectures.net/kdd2014_zhang_link_prediction/ |
| 主讲教师: | Jiawei Zhang |
| 开课单位: | 芝加哥伊利诺伊大学 |
| 开课时间: | 2014-10-07 |
| 课程语种: | 英语 |
| 中文简介: |  提供各种服务的在线社交网络在我们的日常生活中无处不在。同时，现在的用户通常同时参与多个在线社交网络，以享受不同网络提供的特定服务。正式地，共享一些共同用户的社交网络被命名为部分对齐的网络。在本文中，我们要同时预测多个部分对齐的社交网络中社交链接的形成，正式定义为多网络链接（形成）预测问题。在多个部分对齐的社交网络中，用户可以通过各种联系广泛地相互关联。为了对用户之间的这些不同联系进行分类，本文提出了 7 种“网络内社交元路径”和 4 类“网络间社交元路径”。这些“社交元路径”可以涵盖网络中各种各样的连接信息，其中一些可以帮助解决多网络链接预测问题，但有些则不能。为了利用有用的联系，选择了信息量最大的“社会元路径”的子集，其过程在本文中正式定义为“社会元路径选择”。本文提出了一种有效的通用链路形成预测框架Mli（Multi network Link Identifier）来解决多网络链路（形成）预测问题。基于在多个部分对齐的社交网络中基于选定的“社交元路径”提取的异构拓扑特征构建，Mli 可以帮助在所有对齐的网络中相互改进和消除预测结果的歧义。在真实世界部分对齐的异构网络 Foursquare 和 Twitter 上进行的大量实验表明，Mli 可以很好地解决多网络链接预测问题。 |
| 课程简介: | Online social networks offering various services have become ubiquitous in our daily life. Meanwhile, users nowadays are usually involved in multiple online social networks simultaneously to enjoy specific services provided by different networks. Formally, social networks that share some common users are named as partially aligned networks. In this paper, we want to predict the formation of social links in multiple partially aligned social networks at the same time, which is formally defined as the multi-network link (formation) prediction problem. In multiple partially aligned social networks, users can be extensively correlated with each other by various connections. To categorize these diverse connections among users, 7 "intra-network social meta paths" and 4 categories of "inter-network social meta paths" are proposed in this paper. These "social meta paths" can cover a wide variety of connection information in the network, some of which can be helpful for solving the multi-network link prediction problem but some can be not. To utilize useful connection, a subset of the most informative "social meta paths" are picked, the process of which is formally defined as "social meta path selection" in this paper. An effective general link formation prediction framework, Mli (Multi-network Link Identifier), is proposed in this paper to solve the multi-network link (formation) prediction problem. Built with heterogenous topological features extracted based on the selected "social meta paths" in the multiple partially aligned social networks, Mli can help refine and disambiguate the prediction results reciprocally in all aligned networks. Extensive experiments conducted on real-world partially aligned heterogeneous networks, Foursquare and Twitter, demonstrate that Mli can solve the multi-network link prediction problem very well. |
| 关 键 词: | 网络链接预测; 异构网络; 社交链接 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-06-09：zyk |
| 最后编审: | 2021-06-09：zyk |
| 阅读次数: | 37 |