Scalable Weibull Graph Attention Autoencoder for Modeling Document Networks

Autor: Wang, Chaojie, Liu, Xinyang, Wang, Dongsheng, Zhang, Hao, Chen, Bo, Zhou, Mingyuan

Although existing variational graph autoencoders (VGAEs) have been widely used for modeling and generating graph-structured data, most of them are still not flexible enough to approximate the sparse and skewed latent node representations, especially

Externí odkaz: http://arxiv.org/abs/2410.09696

Random-walk Based Generative Model for Classifying Document Networks

Autor: Suzuki, Takafumi J.

Document networks are found in various collections of real-world data, such as citation networks, hyperlinked web pages, and online social networks. A large number of generative models have been proposed because they offer intuitive and useful pictur

Externí odkaz: http://arxiv.org/abs/2001.07380

Hierarchical relational models for document networks

Autor: Chang, Jonathan, Blei, David M.

Publikováno v: Annals of Applied Statistics 2010, Vol. 4, No. 1, 124-150

We develop the relational topic model (RTM), a hierarchical model of both network structure and node attributes. We focus on document networks, where the attributes of each document are its words, that is, discrete observations taken from a fixed voc

Externí odkaz: http://arxiv.org/abs/0909.4331

Semi-metric Behavior in Document Networks and its Application to Recommendation Systems

Autor: Rocha, L. M.

Publikováno v: In: Soft Computing Agents: A New Perspective for Dynamic Information Systems. V. Loia (Ed.) International Series Frontiers in Artificial Intelligence and Applications. IOS Press, pp. 137-163, 2002

Recommendation systems for different Document Networks (DN) such as the World Wide Web (WWW) and Digital Libraries, often use distance functions extracted from relationships among documents and keywords. For instance, documents in the WWW are related

Externí odkaz: http://arxiv.org/abs/cs/0309013

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