A Unified Framework for Community Detection and Network Representation Learning
A Unified Framework for Community Detection and Network Representation Learning
复制标题
社区检测和网络表示学习的统一框架
DOI：
10.1109/tkde.2018.2852958
复制
发表时间：
2016-11
影响因子：
8.9
通讯作者：
Lin Leyu
中科院分区：
文献类型：
--
作者：
Tu Cunchao;Zeng Xiangkai;Wang Hao;Zhang Zhengyan;Liu Zhiyuan;Sun Maosong;Zhang Bo;Lin Leyu
Network representation learning (NRL) aims to learn low-dimensional vectors for vertices in a network. Most existing NRL methods focus on learning representations from local context of vertices (such as their neighbors). Nevertheless, vertices in many complex networks also exhibit significant global patterns widely known as communities. It's intuitive that vertices in the same community tend to connect densely and share common attributes. These patterns are expected to improve NRL and benefit relevant evaluation tasks, such as link prediction and vertex classification. Inspired by the analogy between network representation learning and text modeling, we propose a unified NRL framework by introducing community information of vertices, named as Community-enhanced Network Representation Learning (CNRL). CNRL simultaneously detects community distribution of each vertex and learns embeddings of both vertices and communities. Moreover, the proposed community enhancement mechanism can be applied to various existing NRL models. In experiments, we evaluate our model on vertex classification, link prediction, and community detection using several real-world datasets. The results demonstrate that CNRL significantly and consistently outperforms other state-of-the-art methods while verifying our assumptions on the correlations between vertices and communities.
登录
查看更多内容
DOI：
10.1007/978-1-4419-7142-5_16
发表时间：
2010
期刊：
--
影响因子：
--
作者：
Bo Yang;Da-you Liu;Jiming Liu
通讯作者：
Bo Yang;Da-you Liu;Jiming Liu
DOI：
10.1145/1557019.1557109
发表时间：
2009-06
期刊：
--
影响因子：
--
作者：
Lei Tang;Huan Liu
通讯作者：
Lei Tang;Huan Liu
DOI：
10.1145/2939672.2939754
发表时间：
2016-08
期刊：
KDD : proceedings. International Conference on Knowledge Discovery & Data Mining
影响因子：
--
作者：
Grover A;Leskovec J
通讯作者：
Leskovec J
影响因子：
3.4
作者：
Chen, Zhengzhang;Hendrix, William;Samatova, Nagiza F.
通讯作者：
Samatova, Nagiza F.
DOI：
--
发表时间：
2012-12
期刊：
--
影响因子：
--
作者：
Michael Fire;Gilad Katz;Y. Elovici
通讯作者：
Michael Fire;Gilad Katz;Y. Elovici