Selecting relevant data subsets from large, unfamiliar datasets can be
d...
Effective figure captions are crucial for clear comprehension of scienti...
In this work, we introduce a hypergraph representation learning framewor...
Graph Neural Networks (GNNs) have become increasingly important in recen...
Learning fair graph representations for downstream applications is becom...
Online shopping gives customers boundless options to choose from, backed...
Designing responsive visualizations can be cast as applying transformati...
Conversations aimed at determining good recommendations are iterative in...
Although we have seen a proliferation of algorithms for recommending
vis...
Well-designed data visualizations can lead to more powerful and intuitiv...
We have developed a conversational recommendation system designed to hel...
Visualization recommendation systems simplify exploratory data analysis ...
Visualization recommendation work has focused solely on scoring
visualiz...
In this paper, we introduce a generalization of graphlets to heterogeneo...
Visualization recommendation seeks to generate, score, and recommend to ...
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...
Higher-order connectivity patterns such as small induced sub-graphs call...
Following the success of deep convolutional networks in various vision a...
Graph-structured data arise naturally in many different application doma...
This paper describes a general framework for learning Higher-Order Netwo...