activity
20172021
most citedGraph Neural Networks with Local Graph Parameters

15 citations · 17 across the 5 of their papers we have counts for

collaborators

6 papers

cs.LG202115 cited

Graph Neural Networks with Local Graph Parameters

Pablo Barceló, Floris Geerts, Juan Reutter +1

Various recent proposals increase the distinguishing power of Graph Neural Networks GNNs by propagating features between -tuples of vertices. The distinguishing power of these "…

cs.DB2020

Recursive SPARQL for Graph Analytics

Aidan Hogan, Juan Reutter, Adrian Soto

Work on knowledge graphs and graph-based data management often focus either on declarative graph query languages or on frameworks for graph analytics, where there has been little w…

cs.DB2019

Optimal Joins using Compact Data Structures

Gonzalo Navarro, Juan L. Reutter, Javiel Rojas-Ledesma

Worst-case optimal join algorithms have gained a lot of attention in the database literature. We now count with several algorithms that are optimal in the worst case, and many of t…

cs.DB20171 cited

Assessing Achievability of Queries and Constraints

Rada Chirkova, Jon Doyle, Juan L. Reutter

Assessing and improving the quality of data in data-intensive systems are fundamental challenges that have given rise to numerous applications targeting transformation and cleaning…

cs.DB20171 cited

DataSlicer: Task-Based Data Selection for Visual Data Exploration

Farid Alborzi, Surajit Chaudhuri, Rada Chirkova +5

In visual exploration and analysis of data, determining how to select and transform the data for visualization is a challenge for data-unfamiliar or inexperienced users. Our main h…

cs.DB2017

A Framework for Assessing Achievability of Data-Quality Constraints

Rada Chirkova, Jon Doyle, Juan L. Reutter

Assessing and improving the quality of data are fundamental challenges for data-intensive systems that have given rise to applications targeting transformation and cleaning of data…