The focus of the GRADES-NDA workshop is the application areas, usage scenarios and open challenges in managing large-scale graph-shaped data. The workshop is a forum for exchanging ideas and methods for mining, querying and learning with real-world network data, developing new common understandings of the problems at hand, sharing of data sets and benchmarks where applicable, and leveraging existing knowledge from different disciplines. Additionally, considering specific techniques (e.g., algorithms, data/index structures) in the context of the systems that implement them, GRADES-NDA aims to present technical contributions inside graph, RDF, and other data management systems on graphs of a large size.
Call for Papers
The goal of GRADES-NDA is to bring together researchers from academia, industry, and government, (1) to create a forum for discussing recent advances in (large-scale) graph data management and analytics systems, as well as propose and discuss novel methods and techniques towards (2) addressing domain specific challenges or (3) handling noise in real-world graphs.
The workshop will be of interest to researchers in the development of novel data-management applications and systems for large-scale graph analytics. More specifically, the intended audience are, but not limited to, academic and industrial computer scientists interested in databases and data mining, machine learning, data streaming, graph theory and algorithms. Along with novel research work, we encourage submissions with demonstrations and case studies from real-life experiences in various domains such as Social Networks, Biological Network Data, Marketing and Media, Business Data Analysis, Healthcare Data, Cybersecurity etc.
Topics of interest include but are not limited to the following.
- Graph query languages, visualization techniques and querying interfaces, and their effective realization
- Graph platform and parallel platforms, e.g., Flink/Gelly, Titan, SPARK/GraphX, GraphLab/PowerGraph, Giraph, GraphChi etc.
- Network data representation, storage, indexing and querying methods.
- Experiences or techniques for graph specific operations such as traversals or inference/reasoning in the context of large data sets and on the systems that implement those operations.
- RDF data management and analytics
- Dynamic Graphs: managing graph updates; graph stream analytics; analyzing evolution and detection of community structures in real-world evolving graphs
- Mining and machine learning on heterogeneous networks -- knowledge graphs etc.
- Graph summarization and sampling
- Game Theory, Social contagion and Information propagation on networks
- Analytics on dirty, noisy, or uncertain graphs
- Spatial and temporal graph analytics
- Analytics on social, biological, retail, marketing, customer care, financial, healthcare, transportation network data sets
- Descriptions of graph data management use cases and query workloads, and experiences with applying data management technologies in such situations
- Vision and systems papers describing potential or real applications and benefits of graph management
Accepted papers will be published by ACM, indexed by DBLP, and would be available in the ACM DL.
- Paper Submission: March 22, 2021
- Notifications: April 12, 2021
- Camera Ready Submission: April 26, 2021
- Workshop Date: June 20, 2021
Authors are invited to submit original, unpublished research papers (full and short), demonstrations and case-studies.
Submissions must follow the latest 2-column ACM Master article "sigconf" proceedings LaTeX template with 10pt font size, and should be double-blind. Details on the Anonymity requirements for submitted manuscripts are present at the SIGMOD 2021 Call for Papers page.
- Full papers should be a maximum of 8 pages in length, excluding references and appendix.
- Case studies should be a maximum of 4 pages in length, excluding references and appendix.
- Short papers and demonstration papers should be a maximum of 4 pages in length, excluding references and appendix.
- Renzo Angles, Universidad de Talca
- Alex Averbuch, Neo Technology
- Yang Cao, The University of Edinburgh
- Stefania Dumbrava, ENSIIE Paris-Evry, France
- Irini Fundulaki, ICS-FORTH
- Sainyam Galhotra, University of Massachusetts Amherst
- Joan Guisado-Gámez, Universitat Politècnica de Catalunya
- Russ Harmer, CNRS & ENS Lyon
- Jan Hidders, Birkbeck College, University of London
- Adriana Iamnitchi, University of South Florida
- Panos Kalnis, King Abdullah University of Science and Technology
- Zoi Kaoudi, TU Berlin
- Anil Pacaci, University of Waterloo
- Marcus Paradies, DLR
- Semih Salihoglu, University of Waterloo
- A. Erdem Sarıyüce, University at Buffalo
- Juan F. Sequeda, data.world
- Julian Shun, Massachusetts Institute of Technology
- Andreas Spitz, Ecole Polytechnique Fédérale de Lausanne
- Gábor Szárnyas, Budapest University of Technology and Economics
- Hannes Voigt, Neo4j
- Yinglong Xia, Facebook
- Oskar van Rest, Oracle
GRADES-NDA is in its third edition, and had successful joint meetings collocated with SIGMOD/PODS 2018, 2019, and 2020 respectively. Specifically, it is the merger of the GRADES and NDA workshops, which were each independently organized and successfully held at previous SIGMOD-PODS meetings, GRADES since 2013 and NDA since 2016. The organizers of GRADES and NDA mutually agreed upon to aim for a joint meeting from 2018 onwards.