6,583 research outputs found
Topic Similarity Networks: Visual Analytics for Large Document Sets
We investigate ways in which to improve the interpretability of LDA topic
models by better analyzing and visualizing their outputs. We focus on examining
what we refer to as topic similarity networks: graphs in which nodes represent
latent topics in text collections and links represent similarity among topics.
We describe efficient and effective approaches to both building and labeling
such networks. Visualizations of topic models based on these networks are shown
to be a powerful means of exploring, characterizing, and summarizing large
collections of unstructured text documents. They help to "tease out"
non-obvious connections among different sets of documents and provide insights
into how topics form larger themes. We demonstrate the efficacy and
practicality of these approaches through two case studies: 1) NSF grants for
basic research spanning a 14 year period and 2) the entire English portion of
Wikipedia.Comment: 9 pages; 2014 IEEE International Conference on Big Data (IEEE BigData
2014
Leveraging Citation Networks to Visualize Scholarly Influence Over Time
Assessing the influence of a scholar's work is an important task for funding
organizations, academic departments, and researchers. Common methods, such as
measures of citation counts, can ignore much of the nuance and
multidimensionality of scholarly influence. We present an approach for
generating dynamic visualizations of scholars' careers. This approach uses an
animated node-link diagram showing the citation network accumulated around the
researcher over the course of the career in concert with key indicators,
highlighting influence both within and across fields. We developed our design
in collaboration with one funding organization---the Pew Biomedical Scholars
program---but the methods are generalizable to visualizations of scholarly
influence. We applied the design method to the Microsoft Academic Graph, which
includes more than 120 million publications. We validate our abstractions
throughout the process through collaboration with the Pew Biomedical Scholars
program officers and summative evaluations with their scholars
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