The 9th International Workshop on Graph Data Management and Analysis (GDMA 2025)

May 25-28, 2025, Singapore In conjunction with the 30th International Conference on Database Systems for Advanced Applications (DASFAA 2025)

Recently, there has been a lot of interest in the application of graphs in different domains. They have been widely used for data modeling of different application domains such as multimedia databases, protein networks, social networks and semantic web. With the continued emergence and increase of massive and complex structural graph data, a graph database that efficiently supports elementary data management mechanisms is crucially required to effectively understand and utilize any collection of graphs. The overall goal of the workshop is to bring people from different fields together, exchange research ideas and results, and encourage discussion about how to provide efficient graph data management techniques in different application domains and to understand the research challenges of such area. The first international workshop on graph data management and analysis (GDMA 2017) was held in Beijing, China in conjunction with APWeb-WAIM 2017 and GDMA 2018-2024 was held in conjunction with DASFAA 2018-2023, respectively. The theme of GDMA 2024 is “LLMs-based Graph Management and Analysis”. Having the workshop collocated with DASFAA as a leading database conference will definitely help to achieve the main goals of the workshop.

The overall goal of the workshop is to:

  • Bring people from different fields together.
  • Exchange research ideas and results.
  • Encourage discussions about how to provide efficient graph data management techniques in different application domains.
  • Understand the research challenges of this area.

Call for Papers

Aim of the Workshop
Recently, there has been a lot of interest in the application of graphs in different domains. They have been widely used for data modeling of different application domains such as multimedia databases, protein networks, social networks and semantic web. With the continued emergence and increase of massive and complex structural graph data, a graph database that efficiently supports elementary data management mechanisms is crucially required to effectively understand and utilize any collection of graphs. The overall goal of the workshop is to bring people from different fields together, exchange research ideas and results, and encourage discussion about how to provide efficient graph data management techniques in different application domains and to understand the research challenges of such area. The first international workshop on graph data management and analysis (GDMA 2017) was held in Beijing, China in conjunction with APWeb-WAIM 2017 and GDMA 2018-2024 was held in conjunction with DASFAA 2018-2023, respectively. The theme of GDMA 2024 is “LLMs-based Graph Management and Analysis”. Having the workshop collocated with DASFAA as a leading database conference will definitely help to achieve the main goals of the workshop.

Topics of Interest

Topics of interest include, but are not limited to:

  • Storing graph data.
  • Indexing graph data.
  • Supporting different types of graph queries.
  • Estimating the selectivity of graph queries.
  • Graph mining.
  • Graph learning.
  • Compact (compressed) representation of graph data.
  • Measuring graph similarity.
  • Graph query languages.
  • Graph question answering.
  • Graph data management for social network applications.
  • Graph data management of multimedia databases.
  • Graph data management of semantic web data.
  • Graph data management for geometrical applications.
  • Graph data management for business process management applications.
  • Graph data management with LLMs.
  • Graph learning with LLMs.
  • Graph analysis with LLMs.
  • Advanced applications and tools for managing graph databases in different domains.

Important Dates

  • Deadline for Paper Submission: Jan 30, 2025 (11:59pm PST)
  • Authors Notification: Feb 25, 2025
  • Camera-ready Due: Feb 28, 2025 (11:59pm PST)
  • Workshop Dates: May 24, 2025

Program Committee

TBA

Paper Submission

The review process is single-blinded. There is no need for authors to mask their names and affiliations in the manuscript. The maximal length of the paper is 16 pages.

All papers must be submitted through EasyChair system, via the following link. The submission site is now open.

https://easychair.org/conferences/?conf=gdma2025

The conference proceedings, including all accepted papers, will be published in the Springer Lecture Notes in Computer Science (LNCS) series. Authors should avoid the use of non-English fonts to avoid problems with printing and viewing the submissions. All accepted papers MUST follow strictly the instructions for LNCS Authors. Springer LNCS site offers style files and information: http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0

Submissions must be original (not previously published and not under review in other forums). This applies to papers on all tracks of the conference. Authors are advised to interpret these limitations strictly and to contact the PC chairs in case of doubt. Each accepted paper must be accompanied by at least one full registration, and an author is expected to present the paper at the conference, otherwise, the paper will be removed from the proceedings and the LNCS digital library.

The review process is single-blinded. There is no need for authors to mask their names and affiliations in the manuscript. The maximal length of the paper is 16 pages.

Contact

General Chair

Lei Zou
Peking University, China
Email: zoulei@icst.pku.edu.cn

PC Chairs

Liang Hong
Wuhan University, China
Email: hong@whu.edu.cn

Xiaowang Zhang
Tianjin University, China
Email: xiaowangzhang@tju.edu.cn

Weiguo Zheng
Fudan University, China
Email: zhengweiguo@fudan.edu.cn

DASFAA 2025

Click here to view the program details

GDMA 2025 Program
(Monday 26 2025)

9:00 -10:30am keynote Dr. Ming Hu Optimization Algorithm and Framework Design for Federated Learning

11:00–11:45am Lixing Zhang, Guanhua Ye, Hongzheng Li, Shigang Li and Yingxia Shao. ParamSpMM: Adaptive and Efficient Sparse Matrix-Matrix Multiplication on GPUs for GNNs
11:45–12:30am Ran Liu, Zhongzhou Liu, Xiaoli Li, Hao Wu and Yuan Fang. Diversified and Adaptive Negative Sampling on Knowledge Graphs

Keynote speaker bio
Dr. Ming Hu
He is a research scientist at Singapore Management University. Previously, he was a research fellow at Nanyang Technological University (NTU), Singapore. His research interests include the design and construction of AI software and systems, federated learning, and Trustworthy AI. He has published 30+ research papers in top conferences or journals, such as RTSS, DAC, ICDE, KDD, ICSE, FSE, NeurIPS, TC, and TCAD. He has won the FSE2024 “Distinguished Paper Award”.

Title:
Optimization Algorithm and Framework Design for Federated Learning
Abstract:
Federated Learning (FL) has emerged as a promising paradigm for distributed machine learning, enabling model training across distributed devices while preserving data privacy. However, challenges such as heterogeneous data distributions, resource-constrained devices, and uncertain physical environments hinder its widespread adoption. In this talk, we explore how to design the optimization algorithm and framework of federated learning to address these challenges. Specifically, this talk will explore how to solve the problem of data heterogeneity using the heuristic searching strategies from the perspective of the loss landscape. In addition, this talk will introduce how to design the federated learning framework using asynchronous training and model heterogeneity strategies to address the problem of performance degradation caused by limited device resources and uncertain environments.