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 graph 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.

Proceedings

The proceedings of the workshop are published in the ACM Digital Library under the DOI 10.1145/3594778.

Keynote Speakers

We are very honored to host the following two keynote speakers this year.

• Amy Hodler  RelationalAI
  Amy is an evangelist for graph analytics and responsible AI. She is also the co-author of O’Reilly books on Graph Algorithms and Knowledge Graphs as well as a contributor to the Routledge book, Massive Graph Analytics. Amy has decades of experience in emerging tech at companies such as Microsoft, Hewlett-Packard (HP), Hitachi IoT, Neo4j, and Cray. At RelationalAI, she is the Graph Evangelist and Sr. Director of Product Marketing. Amy recently founded GraphGeeks.org to bring together graph enthusiastics and highlight their work.
• James Cheng  The Chinese University of Hong Kong (CUHK) & Kasma Pte Ltd
  Prof. James Cheng is currently with the Department of Computer Science and Engineering at the Chinese University of Hong Kong and his research focuses on developing systems for graph data management, graph computing, and graph machine learning. Many of the systems developed by his team have been deployed in various large-scale industrial production environments. Prof. Cheng is also the founder of a startup company specializing in graph database and graph data analytics.

Program

All times are in PDT (Pacific Daylight Time, the local time in Seattle).

9:00 AM - 9:05 AM Welcome

9:05 AM - 10:05 AM Keynote by James Cheng (CUHK & Kasma Pte Ltd): Building a Platform for Graph Feature Management Abstract: Graph features are crucial to many applications such as recommender systems and risk management systems. The process to obtain useful graph features involves ingesting data from various upstream data sources, defining the desired graph features for the required applications, constructing a feature engineering workflow to compute the features, and storing and managing the resulting features for downstream tasks (e.g., graph AI and graph BI) and for future reuse. To the majority of users, especially SMEs and non-tech companies, this process poses daunting challenges as it requires users to not only learn various methods (e.g., graph analytical algorithms, non-GNN graph embeddings, GNNs) to define graph features and program their computation, but also learn many infrastructures (e.g., upstream databases, downstream ML systems, graph analytics systems) to compute, manage and use the graph features in production. These challenges have significantly restricted the wider applications of graph technologies such as graph AI and graph BI currently in industry. The current solution provided by major graph database vendors (e.g., Amazon Neptune, Neo4j, TigerGraph) is to connect various upstream and downstream systems to their own graph database, which is used to compute and manage graph features. However, such a solution ties users to a specific graph infrastructure that may not be the preferred infrastructure and may even require them to re-develop their applications on a new infrastructure. In addition, a specific graph database or infrastructure often does not have the best performance for all workloads and certainly does not support the computation of all types of graph features. As a result, the existing solution limits users' flexibility in choosing their own infrastructure and their productivity in developing their applications.
In Part 1 of this talk, I will introduce various types of graph features and their applications. Then I will present some trends in using graph databases for graph feature computation and management, analyze the limitations of the existing methods, and identify the requirements of a graph feature management solution that is practical and highly usable to average users. In Part 2 of this talk, I will introduce our ongoing project that aims at providing a highly usable graph feature platform. Our solution decouples graph feature logic specification and management (i.e., how features are defined, coded and managed) from the generation and execution of the workflow for feature computation (i.e., execution plan generation and the actual execution), so that users can flexibly select different infrastructures suitable for the computation of specific types of graph features. It also manages the upstream, downstream and feature engineering and serving infrastructures, so as to free users from tedious tasks associated with deploying infrastructures and connecting them in a feature engineering dataflow. Thus, users can focus on creating and delivering innovative feature workflow logic. Finally, I will also highlight some possible future directions about graph feature management. Speaker: Prof. James Cheng is currently with the Department of Computer Science and Engineering at the Chinese University of Hong Kong and his research focuses on developing systems for graph data management, graph computing, and graph machine learning. Many of the systems developed by his team have been deployed in various large-scale industrial production environments. Prof. Cheng is also the founder of a startup company specializing in graph database and graph data analytics.

10:05 AM - 10:30 AM Paper Presentation

• Future-Time Temporal Path Queries Christos Gkartzios and Evaggelia Pitoura

11:00 AM - 12:30 PM Paper Presentations

• Going with the Flow: Real-Time Max-Flow on Asynchronous Dynamic Graphs Juntong Luo, Scott Sallinen, and Matei Ripeanu
• Fast Synthetic Data-Aware Log Generation for Temporal Declarative Models Giacomo Bergami
• Learning Graph Neural Networks using Exact Compression Jeroen Bollen, Jasper Steegmans, Jan Van den Bussche, and Stijn Vansummeren

1:30 PM - 2:30 PM Keynote by Amy Hodler (RelationalAI): The Commercial Side of Graph Analytics: Big Uses, Big Mistakes, Big Opportunities Abstract: Connectivity is the cornerstone of our contemporary world, permeating various sectors like retail, communications, biology, and finance. Although this inherent interconnectedness holds substantial meaning and predictive power, harnessing it for practical use in the business realm often proves challenging. In this presentation, we will delve into the commercial applications of graph analytics, highlighting both common pitfalls to avoid and promising opportunities to explore.
To begin, we will explore the prevalent use cases of graph analytics, encompassing areas such as fraud detection, supply chain optimization, data management, and recommendations. We'll also shed light on why many teams tend to deploy only a limited set of graph algorithms. Additionally, we will examine how the COVID-19 pandemic has impacted the utilization of graphs in business settings.
Next, we will venture into the major mistakes that businesses often make when implementing graph analytics. These blunders range from technical hurdles like scalability issues and handling tricky data types to human challenges such as fostering a graph-thinking mindset and avoiding excessive perfectionism. Moreover, you will gain quick tips to help teams secure funding for graph projects.
Lastly, we will delve into some of the most significant prospects within the commercial space. We will address enduring challenges, such as transforming business data into a graph format and ensuring interoperability with production processes. We will also dedicate time to exploring the rising interest in combining graphs with AI systems, particularly the recent buzz surrounding combining graphs with generative AI. While this particular trend garners attention, we will look at other promising opportunities that it may overshadow.
By the end of this talk, you will have gained a comprehensive understanding of the practical applications of graph analytics in business contexts. Furthermore, you'll gain valuable knowledge about pitfalls to avoid, strategies for securing funding, and a forward-looking perspective on emerging possibilities in this dynamic field. Speaker: Amy is an evangelist for graph analytics and responsible AI. She is also the co-author of O’Reilly books on Graph Algorithms and Knowledge Graphs as well as a contributor to the Routledge book, Massive Graph Analytics. Amy has decades of experience in emerging tech at companies such as Microsoft, Hewlett-Packard (HP), Hitachi IoT, Neo4j, and Cray. At RelationalAI, she is the Graph Evangelist and Sr. Director of Product Marketing. Amy recently founded GraphGeeks.org to bring together graph enthusiastics and highlight their work.

2:30 PM - 3:00 PM Paper Presentation

• Better Distributed Graph Query Planning With Scouting Queries Tomáš Faltín, Vasileios Trigonakis, Ayoub Berdai, Luigi Fusco, Călin Iorgulescu, Sungpack Hong, and Hassan Chafi

3:30 PM - 4:10 PM Demo Presentations

• Interpretability Methods for Graph Neural Networks Ehsan Bonabi Mobaraki and Arijit Khan
• EAGER: Explainable Question Answering Using Knowledge Graphs Andrew Chai, Alireza Vezvaei, Lukasz Golab, Mehdi Kargar, Divesh Srivastava, Jaroslaw Szlichta, and Morteza Zihayat

4:10 PM - 5:00 PM Invited Talks

• Distributed temporal graph analytics with GRADOOP Christopher Rost related VLDBJ article
• A General Cardinality Estimation Framework for Subgraph Matching in Property Graphs George Fletcher related TDKE article

Program Committee

• Renzo Angles, Universidad de Talca
• Marcelo Arenas, PUC Chile
• Akhil Arora, Ecole Polytechnique Fédérale de Lausanne
• Amitabha Bagchi, Indian Institute of Technology, Delhi
• Srikanta Bedathur, IIT Delhi
• Yang Cao, The University of Edinburgh
• Juan Colmenares, LinkedIn
• Amol Deshpande, University of Maryland
• Sourav Dutta, Huawei Research
• George H. L. Fletcher, Eindhoven University of Technology
• Irini Fundulaki, ICS-FORTH
• Russ Harmer, CNRS & ENS Lyon
• Jan Hidders, Birkbeck College, University of London
• Alexandru Iosup, VU
• Panos Kalnis, King Abdullah University of Science and Technology
• Zoi Kaoudi, IT University of Copenhagen
• Arijit Khan, Nanyang Technological University
• Josep Lluís Larriba-Pey, Universitat Politècnica de Catalunya
• Makoto Onizuka, Osaka University
• Anil Pacaci, Apple
• Evaggelia Pitoura, Univ. of Ioannina
• Matei Ripeanu, The University of British Columbia
• Petra Selmer, Bloomberg
• Marco Serafini, University of Massachusetts Amherst
• Hiroaki Shiokawa, University of Tsukuba
• Vasileios Trigonakis, Oracle Labs
• Ana Lucia Varbanescu, University of Amsterdam
• Hannes Voigt, Neo4j
• Yinghui Wu, Case Western Reserve University
• Yinglong Xia, Facebook
• Shangdi Yu, MIT
• Wenjie Zhang, The University of New South Wales