About
Graph, relational, heterogeneous, and structured data are central to modern data mining, with applications spanning web platforms, recommender systems, finance, cybersecurity, science, and cyber-physical systems. Such data are often large-scale, interconnected, and complex, and many real-world settings also involve streaming updates, distribution shift, and long-term evolution. These challenges call for advances not only in graph mining and learning, but also in broader methods for relational reasoning, discovery, retrieval, recommendation, system support, and reliable evaluation.
The First ICDM Workshop on Evolving Graph Data Mining and Continual Relational Learning (EvoGraphDM) aims to provide a focused yet inclusive forum for researchers and practitioners working on data mining and learning for graph, relational, heterogeneous, and structured data. While the workshop is motivated by evolving graph data mining and continual relational learning, it is intentionally broader than a graph-only or temporal-only venue. We welcome contributions on dynamic, streaming, continual, and non-stationary settings, as well as broader methods, systems, and applications that are relevant to interconnected and structured data environments, including work that may not be explicitly graph-centric. EvoGraphDM seeks to encourage interaction across data mining, machine learning, data management, information retrieval, and real-world application domains, and to highlight research that is both methodologically strong and practically useful.
Topics of Interest
We welcome submissions on graph, relational, heterogeneous, and structured data mining and learning more broadly, including but not limited to evolving, temporal, streaming, continual, and non-stationary settings. Topics of interest include, but are not limited to:
- Data Mining and Learning for Graph, Relational, and Structured Data
- Knowledge Graphs, Heterogeneous Data, and Relational Intelligence
- Analytics, Discovery, Retrieval, Recommendation, and Visualization
- Dynamic, Streaming, Continual, and Non-stationary Data Mining
- Systems, Scalability, Distributed Computing, and Evaluation
- Applications in Web, Social, Recommender, Scientific, and Cyber-Physical Systems
Important Dates
Workshop Paper Submission: August 20, 2026
Notification of Acceptance: September 18, 2026
Camera-ready Deadline and Copyright Form: TBA
All deadlines are 11:59 PM
AOE (Anywhere on Earth).
Submission Guidelines
Papers submitted to the workshop will undergo peer review. Submissions should be no more than 8 pages, with up to 2 additional pages for references. Accepted workshop papers will be published in the dedicated ICDMW proceedings by the IEEE Computer Society Press. For each accepted paper, at least one author must pay a full author registration and attend the conference to present the work on site.
Workshop Program
Keynote Speakers
Organizers
Xiaofan Li
Nanjing University
Xiaofan Li is an Assistant Professor and Ph.D. supervisor with the School of Computer Science, Nanjing University. His research lies at the intersection of databases, graph-structured data, and large language models, with particular interests in graph databases, explainable graph neural networks, and data management methods for emerging AI systems. Before joining Nanjing University, he worked at Nanyang Technological University and the L3S Research Center at Leibniz University Hannover, and he was recognized as a DAAD AInet Fellow. He has published in leading venues such as SIGMOD, ICDE, WWW, and ICDM, and has contributed to the research community through conference and journal reviewing as well as conference organization, including serving as a proceeding co-chair of HIS 2024. His background is closely aligned with EvoGraphDM, especially in graph-structured data management, temporal and relational learning over structured data, and the interface between graph mining and modern AI.
Haipeng Dai
Nanjing University
Haipeng Dai is a Professor and Ph.D. supervisor with the School of Computer Science, Nanjing University, and a member of the Laboratory of Advanced Networking and Data Science (LANDS). His research spans network data mining, edge intelligence, large-model collaboration, intelligent IoT, mobile computing, and cyber-physical systems, all with clear relevance to data-driven systems operating in dynamic real-world environments. He has published extensively in leading venues such as INFOCOM, SIGMOD, VLDB, KDD, ICDE, WWW, UbiComp, TON, TKDE, and TMC, and has received multiple recognitions including the Young Chang Jiang Scholar award. He brings a strong data mining and systems perspective to EvoGraphDM, particularly for evolving relational structures arising in networked, intelligent, and cyber-physical systems.
Rui Zhou
Swinburne University of Technology
Rui Zhou is a Senior Lecturer in the Department of Computing Technologies at Swinburne University of Technology. His research areas include data management and data science, and his recent publication record spans graph-structured and relational data problems, including temporal and heterogeneous information networks, knowledge-graph-related mining, and graph-oriented optimization. His work has appeared in venues such as SIGMOD, VLDB, ICDE, KDD, and ICDM, as well as related international forums, through sustained collaborations on graph and data management research. He brings a strong international perspective to EvoGraphDM and complements the organizer team with expertise in structured data, graph-oriented data mining, temporal relational analysis, and academic outreach across Australia and the broader international community.