output
20202026
most citedThe Future is Big Graphs! A Community View on Graph Processing Systems

157 citations

6 papers

cs.CL2026

Linguistic Signatures for Enhanced Emotion Detection

Florian Lecourt, Madalina Croitoru, Konstantin Todorov

Emotion detection is a central problem in NLP, with recent progress driven by transformer-based models trained on established datasets. However, little is known about the linguisti…

cs.DB2023

Efficient Enumeration of Recursive Plans in Transformation-based Query Optimizers

Amela Fejza, Pierre Genevès, Nabil Layaïda

Query optimizers built on the transformation-based Volcano/Cascades framework are used in many database systems. Transformations proposed earlier on the logical query dag (LQDAG) d…

cs.DB2021

Distributed Evaluation of Graph Queries using Recursive Relational Algebra

Sarah Chlyah, Pierre Genevès, Nabil Layaïda

We present a system called Dist--RA for the distributed evaluation of recursive graph queries. Dist--RA builds on the recursive relational algebra and extends it with evaluat…

cs.CR2021★ 2 cited

Constrained Differentially Private Federated Learning for Low-bandwidth Devices

Raouf Kerkouche, Gergely Ács, Claude Castelluccia +1

Federated learning becomes a prominent approach when different entities want to learn collaboratively a common model without sharing their training data. However, Federated learnin…

cs.DC2020★ 157 cited

The Future is Big Graphs! A Community View on Graph Processing Systems

Sherif Sakr, Angela Bonifati, Hannes Voigt +38

Graphs are by nature unifying abstractions that can leverage interconnectedness to represent, explore, predict, and explain real- and digital-world phenomena. Although real users a…

cs.DB2020★ 8 cited

Graph Generators: State of the Art and Open Challenges

Angela Bonifati, Irena Holubová, Arnau Prat-Pérez +1

The abundance of interconnected data has fueled the design and implementation of graph generators reproducing real-world linking properties, or gauging the effectiveness of graph a…