Search Results for author:
Found 19 papers, 10 papers with code
Computing Consensus Curves
We consider the problem of extracting accurate average ant trajectories from many (possibly inaccurate) input trajectories contributed by citizen scientists.
On Semantic Word Cloud Representation
We study the problem of computing semantic-preserving word clouds in which semantically related words are close to each other.
Analyzing the Language of Food on Social Media
We investigate the predictive power behind the language of food on social media.
Towards using social media to identify individuals at risk for preventable chronic illness
The strategy uses a game-like quiz with data and questions acquired semi-automatically from Twitter.
Detecting Diabetes Risk from Social Media Activity
This work explores the detection of individuals{'} risk of type 2 diabetes mellitus (T2DM) directly from their social media (Twitter) activity.
Multi-level tree based approach for interactive graph visualization with semantic zoom
A recent data visualization literacy study shows that most people cannot read networks that use hierarchical cluster representations such as "super-noding" and "edge bundling."
Computational Geometry Data Structures and Algorithms Human-Computer Interaction
Symmetry Detection and Classification in Drawings of Graphs
Finally, we make available a collection of images of graph drawings with specific symmetric features that can be used in machine learning systems for training, testing and validation purposes.
Stress-Plus-X (SPX) Graph Layout
Stress, edge crossings, and crossing angles play an important role in the quality and readability of graph drawings.
Computing Stable Demers Cartograms
We enforce orthogonal separation constraints with linear programming, and measure quality in terms of keeping adjacent regions close (cartogram quality) and using similar positions for a region between the different data values (stability).
Computational Geometry Data Structures and Algorithms
Multi-Perspective, Simultaneous Embedding
MPSE with fixed projections takes as input a set of pairwise distance matrices defined on the data points, along with the same number of projections and embeds the points in 3D so that the pairwise distances are preserved in the given projections.
What does the language of foods say about us?
Second, we investigate the effect of socioeconomic factors (income, poverty, and education) on predicting state-level T2DM rates.
The Language of Food during the Pandemic: Hints about the Dietary Effects of Covid-19
Specifically, we analyze over770, 000 tweets published during the lockdown and the equivalent period in the five previous years and highlight several worrying trends.
On the Readability of Abstract Set Visualizations
Set systems are used to model data that naturally arises in many contexts: social networks have communities, musicians have genres, and patients have symptoms.
Human-Computer Interaction
Multi-level Weighted Additive Spanners
In this paper, we consider a multi-level version of the subsetwise spanner in weighted graphs, where the vertices in $S$ possess varying level, priority, or quality of service (QoS) requirements, and the goal is to compute a nested sequence of spanners with the minimum number of total edges.
Discrete Mathematics
Computing Steiner Trees using Graph Neural Networks
Graph neural networks have been successful in many learning problems and real-world applications.
ENS-t-SNE: Embedding Neighborhoods Simultaneously t-SNE
When visualizing a high-dimensional dataset, dimension reduction techniques are commonly employed which provide a single 2-dimensional view of the data.
Nearly Optimal Steiner Trees using Graph Neural Network Assisted Monte Carlo Tree Search
Graph neural networks are useful for learning problems, as well as for combinatorial and graph problems such as the Subgraph Isomorphism Problem and the Traveling Salesman Problem.
Balancing between the Local and Global Structures (LGS) in Graph Embedding
For a given graph, LGS aims to find a good balance between the local and global structure to preserve.
Graph Sparsifications using Neural Network Assisted Monte Carlo Tree Search
Graph neural networks have been successful for machine learning, as well as for combinatorial and graph problems such as the Subgraph Isomorphism Problem and the Traveling Salesman Problem.
