International workshop on Scalable and Deep Graph Learning and Mining (SGLM)

Call for Papers

Graphs serve as flexible and powerful models for representing diverse types of data encountered in modern research and industries. These include the WWW, social networks, biological networks, communication networks, transportation networks, energy grids, and many others. Unlike traditional tabular data formats, graphs enable the representation of entities along with their attributes or properties, as well as the relational structure between entities, making them invaluable for capturing complex data relationships and patterns. Additionally, graphs can accommodate unstructured and heterogeneous data, further enhancing their versatility in handling a wide range of data types and structures.
The significance of extracting knowledge and making predictions from graph data has grown rapidly in recent years. However, there remains a need for ongoing scientific exploration to formalize new problem types that align effectively with real-world applications. Additionally, investigating the algorithmic, statistical, and information-theoretic aspects of these problems is essential for advancing our understanding. Of particular interest to the workshop is the increasingly popular field of graph representation learning. These intermediate real-valued representations enable the application of learning and mining algorithms developed for non-relational data to graph structures. Given the rapid progress in this area, ensuring trustworthy AI on graphs requires focused attention.

The main aim of this workshop, scheduled to be held in conjunction with IEEE BigData, is to serve as a scientific forum for discussing the latest advancements in these areas. We welcome both theoretical and practical contributions, fostering interactions among participants. Additionally, we will schedule conferences or talks specifically focused on these topics to further enrich the discussions.

Topics

We cordially invite submissions covering theoretical aspects, algorithms, methods, and applications within the following (non-exhaustive) list of areas:

  • Computational or statistical learning theory related to graphs.
  • Theoretical analysis of graph algorithms or models.
  • Semi-supervised learning, online learning, active learning, transductive inference, and transfer learning in the context of graphs.
  • Graph and vertex embeddings and representation learning on graphs.
  • Explainable, fair, robust, and/or privacy preserving ML on graphs, and graph sampling.
  • Analysis of social media, chemical or biological networks, infrastructure networks, knowledge graphs.
  • Benchmarking aspects of graph based learning
  • Libraries and tools for all of the above areas.
  • Knowledge graph applications
  • Representation Learning over Knowledge Graphs
  • Dynamic knowledge graphs
  • Large Language Models for Knowledge Graphs
  • Knowledge Graphs for Large Language Models
  • Prompt engineering and knowledge graphs

Paper Submission

Full Papers, up to 8 pages according to IJCNN 2025 paper template: these papers will undergo a regular review process. If accepted, full papers will be published in the IJCNN 2025 proceedings.

Short Papers, up to 4 pages: these papers can be presented at the Workshop but not included in the IJCNN 2025 proceedings.

Full details are available here

All accepted SGLM workshop full papers will be published in the IJCNN 2025 proceedings. The proceedings is available in IEEE Xplore Digital Library indexed by Google Scholar and Scopus.

Submission Guidelines

Important dates

Contact Information

  • Email: sabeur.aridhi@loria.fr, wissem.inoubli@univ-artois.fr, engelbert.mephunguifo@uca.fr, hmezni@taibahu.edu.sa

Past Workshop

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