A generative model of article citation networks of a subject from a large-scale citation database

2021 ◽  
Author(s):  
Livia Lin-Hsuan Chang ◽  
Frederick Kin Hing Phoa ◽  
Junji Nakano
Keyword(s):  
Large Scale ◽  
Generative Model ◽  
Citation Networks ◽  
Citation Database ◽  
Article Citation

scholarly journals AI-Sketcher : A Deep Generative Model for Producing High-Quality Sketches

2019 ◽  
Vol 33 ◽  
pp. 2564-2571 ◽  
Author(s):  
Nan Cao ◽  
Xin Yan ◽  
Yang Shi ◽  
Chaoran Chen
Keyword(s):  
Artificial Intelligence ◽  
Large Scale ◽  
State Of The Art ◽  
Positional Information ◽  
Generative Model ◽  
Training Data ◽  
Creative Design ◽  
High Quality ◽  
User Input ◽  
Variational Autoencoder

Sketch drawings play an important role in assisting humans in communication and creative design since ancient period. This situation has motivated the development of artificial intelligence (AI) techniques for automatically generating sketches based on user input. Sketch-RNN, a sequence-to-sequence variational autoencoder (VAE) model, was developed for this purpose and known as a state-of-the-art technique. However, it suffers from limitations, including the generation of lowquality results and its incapability to support multi-class generations. To address these issues, we introduced AI-Sketcher, a deep generative model for generating high-quality multiclass sketches. Our model improves drawing quality by employing a CNN-based autoencoder to capture the positional information of each stroke at the pixel level. It also introduces an influence layer to more precisely guide the generation of each stroke by directly referring to the training data. To support multi-class sketch generation, we provided a conditional vector that can help differentiate sketches under various classes. The proposed technique was evaluated based on two large-scale sketch datasets, and results demonstrated its power in generating high-quality sketches.

scholarly journals Exploiting citation networks for large-scale author name disambiguation

2014 ◽  
Vol 3 (1) ◽  
Author(s):  
Christian Schulz ◽  
Amin Mazloumian ◽  
Alexander M Petersen ◽  
Orion Penner ◽  
Dirk Helbing
Keyword(s):  
Large Scale ◽  
Citation Networks ◽  
Name Disambiguation ◽  
Author Name Disambiguation

Multi-Label Regularized Generative Model for Semi-Supervised Collective Classification in Large-Scale Networks

2015 ◽  
Vol 2 (4) ◽  
pp. 187-201 ◽  
Author(s):  
Qingyao Wu ◽  
Jian Chen ◽  
Shen-Shyang Ho ◽  
Xutao Li ◽  
Huaqing Min ◽  
...  
Keyword(s):  
Large Scale ◽  
Generative Model ◽  
Collective Classification ◽  
Large Scale Networks

scholarly journals CiteWiz: A Tool for the Visualization of Scientific Citation Networks

2007 ◽  
Vol 6 (3) ◽  
pp. 215-232 ◽  
Author(s):  
Niklas Elmqvist ◽  
Philippas Tsigas
Keyword(s):  
User Study ◽  
Citation Networks ◽  
Graph Layout ◽  
Citation Data ◽  
Citation Database ◽  
Basic Force ◽  
Scientific Citation ◽  
The Individual ◽  
High Level ◽  
Level Analysis

We present CiteWiz, an extensible framework for visualization of scientific citation networks. The system is based on a taxonomy of citation database usage for researchers, and provides a timeline visualization for overviews and an influence visualization for detailed views. The timeline displays the general chronology and importance of authors and articles in a citation database, whereas the influence visualization is implemented using the Growing Polygons technique, suitably modified to the context of browsing citation data. Using the latter technique, hierarchies of articles with potentially very long citation chains can be graphically represented. The visualization is augmented with mechanisms for parent–child visualization and suitable interaction techniques for interacting with the view hierarchy and the individual articles in the dataset. We also provide an interactive concept map for keywords and co-authorship using a basic force-directed graph layout scheme. A formal user study indicates that CiteWiz is significantly more efficient than traditional database interfaces for high-level analysis tasks relating to influence and overviews, and equally efficient for low-level tasks such as finding a paper and correlating bibliographical data.

scholarly journals Assigning credit to scientific datasets using article citation networks

2020 ◽  
Vol 14 (2) ◽  
pp. 101013 ◽  
Author(s):  
Tong Zeng ◽  
Longfeng Wu ◽  
Sarah Bratt ◽  
Daniel E. Acuna
Keyword(s):  
Citation Networks ◽  
Scientific Datasets ◽  
Article Citation

scholarly journals Large-scale structure of time evolving citation networks

2007 ◽  
Vol 59 (1) ◽  
pp. 75-83 ◽  
Author(s):  
E. A. Leicht ◽  
G. Clarkson ◽  
K. Shedden ◽  
M. E.J. Newman
Keyword(s):  
Large Scale ◽  
Large Scale Structure ◽  
Scale Structure ◽  
Citation Networks

scholarly journals Deep Generative Models for 3D Compound Design

2019 ◽  
Author(s):  
Fergus Imrie ◽  
Anthony R. Bradley ◽  
Mihaela van der Schaar ◽  
Charlotte M. Deane
Keyword(s):  
Large Scale ◽  
Structural Information ◽  
Generative Models ◽  
Generative Model ◽  
Machine Learning Techniques ◽  
Generation Process ◽  
Diverse Range ◽  
Partial Structures ◽  
Compound Design ◽  
3D Information

AbstractRational compound design remains a challenging problem for both computational methods and medicinal chemists. Computational generative methods have begun to show promising results for the design problem. However, they have not yet used the power of 3D structural information. We have developed a novel graph-based deep generative model that combines state-of-the-art machine learning techniques with structural knowledge. Our method (“DeLinker”) takes two fragments or partial structures and designs a molecule incorporating both. The generation process is protein context dependent, utilising the relative distance and orientation between the partial structures. This 3D information is vital to successful compound design, and we demonstrate its impact on the generation process and the limitations of omitting such information. In a large scale evaluation, DeLinker designed 60% more molecules with high 3D similarity to the original molecule than a database baseline. When considering the more relevant problem of longer linkers with at least five atoms, the outperformance increased to 200%. We demonstrate the effectiveness and applicability of this approach on a diverse range of design problems: fragment linking, scaffold hopping, and proteolysis targeting chimera (PROTAC) design. As far as we are aware, this is the first molecular generative model to incorporate 3D structural information directly in the design process. Code is available at https://github.com/oxpig/DeLinker.

scholarly journals Large-Scale Survey of Cell-Differentiation Programs in a Generative Model Reveals Regeneration as an Epiphenomenon of Development

2019 ◽  
Author(s):  
Somya Mani ◽  
Tsvi Tlusty
Keyword(s):  
Cell Differentiation ◽  
Large Scale ◽  
Asymmetric Cell Division ◽  
Life Forms ◽  
Directed Acyclic Graphs ◽  
Generative Model ◽  
Whole Body ◽  
Cell Type ◽  
Acyclic Graphs ◽  
Large Scale Survey

SummaryDevelopment combines three basic processes — asymmetric cell division, signaling and gene regulation — in a multitude of ways to create an overwhelming diversity of multicellular life-forms. Here, we attempt to chart this diversity using a generative model. We sample millions of biologically feasible developmental schemes, allowing us to comment on the statistical properties of cell-differentiation trajectories they produce. Our results indicate that, in contrast to common views, cell-type lineage graphs are unlikely to be tree-like. Instead, they are more likely to be directed acyclic graphs, with multiple lineages converging on the same terminal cell-type. Additionally, in line with the hypothesis that whole body regeneration is an epiphenomenon of development, a majority of the ‘organisms’ generated by our model can regenerate using pluripotent cells. The generative framework is modular and flexible, and can be adapted to test additional hypotheses about general features of development.

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