|CRL: Collaborative Representation Learning by Coordinating Topic Modeling and Network Embeddings|
|Chen，Junyang1; Gong，Zhiguo2; Wang，Wei3; Liu，Weiwen4; Dong，Xiao5|
|Source Publication||IEEE Transactions on Neural Networks and Learning Systems|
|Abstract||Network representation learning (NRL) has shown its effectiveness in many tasks, such as vertex classification, link prediction, and community detection. In many applications, vertices of social networks contain textual information, e.g., citation networks, which form a text corpus and can be applied to the typical representation learning methods. The global context in the text corpus can be utilized by topic models to discover the topic structures of vertices. Nevertheless, most existing NRL approaches focus on learning representations from the local neighbors of vertices and ignore the global structure of the associated textual information in networks. In this article, we propose a unified model based on matrix factorization (MF), named collaborative representation learning (CRL), which: 1) considers complementary global and local information simultaneously and 2) models topics and learns network embeddings collaboratively. Moreover, we incorporate the Fletcher-Reeves (FR) MF, a conjugate gradient method, to optimize the embedding matrices in an alternative mode. We call this parameter learning method as AFR in our work that can achieve convergence after a few numbers of iterations. Also, by evaluating CRL on topic coherence and vertex classification using several real-world data sets, our experimental study shows that this collaborative model not only can improve the performance of topic discovery over the baseline topic models but also can learn better network representations than the state-of-the-art context-aware NRL models.|
|Keyword||Collaboration Collaborative representation learning (CRL) Context modeling Correlation Data models Electronic mail global and local contexts Learning systems network embeddings network representation learning (NRL) Network topology topic modeling.|
|URL||View the original|
|Document Type||Journal article|
|Collection||University of Macau|
|Affiliation||1.College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518052, China.|
2.State Key Laboratory of Internet of Things for Smart City, Department of Computer Information Science, University of Macau, Macau (e-mail: email@example.com)
3.School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, China (e-mail: firstname.lastname@example.org)
4.Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong.
5.School of Artificial Intelligence, Sun Yat-sen University, Shenzhen 518107, China.
|Chen，Junyang,Gong，Zhiguo,Wang，Wei,et al. CRL: Collaborative Representation Learning by Coordinating Topic Modeling and Network Embeddings[J]. IEEE Transactions on Neural Networks and Learning Systems,2021.|
|APA||Chen，Junyang,Gong，Zhiguo,Wang，Wei,Liu，Weiwen,&Dong，Xiao.(2021).CRL: Collaborative Representation Learning by Coordinating Topic Modeling and Network Embeddings.IEEE Transactions on Neural Networks and Learning Systems.|
|MLA||Chen，Junyang,et al."CRL: Collaborative Representation Learning by Coordinating Topic Modeling and Network Embeddings".IEEE Transactions on Neural Networks and Learning Systems (2021).|
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