Rapid advancements of large language models (LLMs) have enabled the
proc...
Captions are crucial for understanding scientific visualizations and
doc...
Graph Neural Networks (GNNs) have become increasingly important due to t...
We consider dynamic pricing strategies in a streamed longitudinal data s...
Effective figure captions are crucial for clear comprehension of scienti...
Learning fair graph representations for downstream applications is becom...
Temporal networks model a variety of important phenomena involving timed...
Session-based recommender systems capture the short-term interest of a u...
Bundle recommender systems recommend sets of items (e.g., pants, shirt, ...
How can we predict missing values in multi-dimensional data (or tensors)...
Knowledge graphs suffer from sparsity which degrades the quality of
repr...
Visualization recommendation work has focused solely on scoring
visualiz...
Recently, neural-symbolic architectures have achieved success on commons...
Symbolic knowledge (e.g., entities, relations, and facts in a knowledge
...
In this paper, we introduce a generalization of graphlets to heterogeneo...
Deep probabilistic forecasting techniques have recently been proposed fo...
Visualization recommendation seeks to generate, score, and recommend to ...
We introduce a general framework for leveraging graph stream data for
te...
Roles are sets of structurally similar nodes that are more similar to no...
Community detection in graphs has many important and fundamental applica...
In this paper, we introduce the notion of motif closure and describe
hig...
Figures, such as bar charts, pie charts, and line plots, are widely used...
Networks evolve continuously over time with the addition, deletion, and
...
Many real-world applications give rise to large heterogeneous networks w...
Following the success of deep convolutional networks in various vision a...
Graph-structured data arise naturally in many different application doma...