We will be using official SIGMOD/PODS Whova to run GRADES-NDA online. To obtain access to SIGMOD/PODS Whova, please register here. Alternatively, you can join directly on Zoom (link). For Zoom password, please check the email you received from SIGMOD/PODS organizers.

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.

Keynote Speakers

We are honored to have the following keynote speakers to talk about their exciting research in the broad field of graph data management.

• Angela Bonifati  Lyon 1 University
• Samuel V. Scarpino  Northeastern University

Program

All times are in EDT (Eastern Daylight Time, UTC/GMT -4 hours).

8:10AM-9:00AM Keynote: Graph Processing Systems Back to the Future - Angela Bonifati Abstract: Graphs are data model abstractions that are becoming pervasive in several real-life applications and use cases. In these settings, users focus on entities and their relationships, further enhanced with multiple labels and properties to form the so called property graphs. Modern graph processing systems need to keep pace with the increasing fundamental requirements of these applications and to tackle unforeseen challenges. Motivated by a community vision on future graph processing systems, in this talk I will present the system challenges that are lying behind the current research topics on graph processing and graph analytics. Many current graph query engines support subsets of graph queries that they can efficiently evaluate, thus disregarding more expressive query fragments on top of property graphs. It becomes crucial to address efficient query evaluation for complex graph queries, as well the extensibility of the underlying graph query and constraint languages. Moreover, the dynamic aspects of evaluating queries on streaming graphs are equally important and need to be considered in ongoing and future benchmarking efforts. The overarching goal of my talk is to touch upon our past and ongoing work on these topics and to pinpoint the research directions shaping the already bright future of graph processing systems. Speaker bio: Angela Bonifati is a Professor of Computer Science at Lyon 1 University and affiliated with the CNRS Liris research lab. In 2019 and 2020, she has been on leave at INRIA. Prior to that, she was working as a Professor at Lille 1 University (2011-2015) and as a researcher at CNR, Italy since 2003. She received her Ph.D. from Politecnico di Milano in 2002. Her current research interests are on the inter-play of relational and graph-oriented data paradigms, particularly query processing, data integration and learning for both structured and unstructured data models. She is involved in several grants at Lyon 1 University, including French, EU and industrial grants. She has also co-authored several publications in top venues of the data management field along with two books (edited by Springer in 2011 and Morgan&Claypool in 2018) and an invited paper in ACM Sigmod Record 2018. She is the Program Chair of ACM Sigmod 2022. She is Associate Editor of the VLDB Journal, ACM TODS, Distributed and Parallel Databases and Frontiers in Big Data. She has been the Program Chair of EDBT 2020 and an Associate Editor for Proceedings of VLDB and IEEE ICDE in 2021. She is currently the President of the EDBT Executive Board and a member of the ICDT council. She holds many visiting scholar positions in foreign universities in both Europe and North America. Since 2020, she is also Adjunct Professor at the University of Waterloo in Canada.

9:30AM-10:50AM Session 1 (Research)

• Demystifying Memory Access Patterns of FPGA-Based Graph Processing Accelerators Jonas Dann, Daniel Ritter and Holger Fröning
• Context-Free Path Querying with All-Path Semantics by Matrix Multiplication Rustam Azimov, Ilya Epelbaum and Semyon Grigorev
• A GraphBLAS implementation in pure Java Florentin Dörre, Alexander Krause, Dirk Habich and Martin Junghanns
• Large-scale Influence Maximization with the Influence Maximization Benchmarker Suite Heiko Geppert, Sukanya Bhowmik and Kurt Rothermel

11:00AM-11:50AM Keynote: Networked Data and COVID-19 - Samuel Scarpino Abstract: The COVID-19 pandemic has upended our societies and re-shaped the way we go about our day-to-day lives—from how we work and interact to the way we buy groceries and attend school. Leveraging global data sets that represent billions of people, I will present a series of studies exploring how our behavior, mobility patterns, and social networks have altered and been altered by COVID-19 and the non-pharmaceutical interventions implemented to control its spread. Next, I will examine how we can better incorporate stochasticity and social network heterogeneity and link directionality into forecasting pandemic risk. With these results, I will demonstrate how the complexity of COVID-19 creates epistemological challenges associated with model identifiability. Finally, I will discuss work by Global.health, a new collaborative network of researchers, technologists, and public health experts that has developed and built an open access platform for collecting, storing, securing, and sharing anonymized, individual-level COVID-19 data. Currently, our data includes almost 30M individual-level cases from 160 countries, which are tagged with up to 40 fields of meta-data. Writing for The New York Times Magazine, Steven Johnson said the data captured by Global.health, "may well be the single most accurate portrait of the virus’s spread through the human population in existence." Speaker bio: Samuel V. Scarpino, PhD is an Assistant Professor in the Network Science Institute at Northeastern University and holds academic appointments in Physics, Health Sciences, the Khoury College of Computer Sciences, the Global Resilience Institute, and the Roux Institute. At Northeastern University, he directs the Emergent Epidemics Lab and is a Co-founder of Global.health. Scarpino has 10+ years of experience translating research into decision support and data science/ML tools across diverse sectors from public health and clinical medicine to real estate and energy. From 2017 to 2020, he was Chief Strategy Officer and head of data science at Dharma Platform–a social impact–technology startup. Scarpino has nearly 100 publications in academic journals and books. His expert commentaries on science and technology have appeared in publications such as: Nature, Science, PNAS, and Nature Physics. His research has been covered by the New York Times, Wired, the Boston Globe, NPR, VICE News, National Geographic, and numerous other venues. For his contributions to complex systems science, he was made a Fellow of the ISI Foundation in 2017, an External Faculty member of the Santa Fe Institute in 2020, and an External Faculty member of the Vermont Complex Systems Center in 2021.

12:00PM-12:30PM Session 2 (Industry)

• Neo4j & the Graph Data Science Library Alicia Frame, Director of Graph Data Science, Neo4j
• Graph processing in multi-model workloads using SAP HANA Matthias Hauck, SAP
• GraphScope PatMat: Interactive Mining of Graph Patterns at Scale Zhengping Qian, Alibaba

12:30PM-1:30PM Lunch + free-roam posters and demos

Demos:

• End to End Graph Native Machine Learning with Neo4j Stuart Laurie, Neo4j
• GraphScope: Towards a Swiss Army Knife for a Continuous Life Cycle of Big Graph Analytics Wenyuan Yu, Alibaba

Posters:

• Demystifying Memory Access Patterns of FPGA-Based Graph Processing Accelerators Jonas Dann, Daniel Ritter and Holger Fröning
• Context-Free Path Querying with All-Path Semantics by Matrix Multiplication Rustam Azimov, Ilya Epelbaum and Semyon Grigorev
• A GraphBLAS implementation in pure Java Florentin Dörre, Alexander Krause, Dirk Habich and Martin Junghanns
• Large-scale Influence Maximization with the Influence Maximization Benchmarker Suite Heiko Geppert, Sukanya Bhowmik and Kurt Rothermel
• Position Paper: Bitemporal Dynamic Graph Analytics Hassan Halawa and Matei Ripeanu
• LSQB: A Large-Scale Subgraph Query Benchmark Amine Mhedhbi, Matteo Lissandrini, Laurens Kuiper, Jack Waudby and Gábor Szárnyas
• R2GSync and Edge Views: Practical RDBMS to GDBMS Synchronization Nafisa Anzum and Semih Salihoglu

1:30PM-2:30PM Session 3 (Research)

• Position Paper: Bitemporal Dynamic Graph Analytics Hassan Halawa and Matei Ripeanu
• LSQB: A Large-Scale Subgraph Query Benchmark Amine Mhedhbi, Matteo Lissandrini, Laurens Kuiper, Jack Waudby and Gábor Szárnyas
• R2GSync and Edge Views: Practical RDBMS to GDBMS Synchronization Nafisa Anzum and Semih Salihoglu

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

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.

Length Requirements:

• 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.

Submissions will be handled through Easychair. To submit click here.

Program Committee

• 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

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