Date : 2024-12-17

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
The desired workshop format is full day. The workshop accepts two types of submissions:

Long papers (full research papers / up to 10 pages (references included), in the IEEE 2-column format)
Short papers of 2-4 pages (for work in progress)
Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines (https://www.ieee.org/conferences/publishing/templates.html).
The submission deadline is October 01, 2024 for abstracts and for full papers.
Submissions should be made through cyberchair at:

Paper Submission Page

We also invite the submission of 2-4 pages extended abstracts as highlight papers or late breaking research papers. Highlight papers should summarize full papers that have been published, or accepted for publication.

Description of the submission review process, including key dates and coverage of how conflicts of interest are handled

Papers will be subject to three (3) blind peer reviews. Selection criteria include originality of ideas, correctness, clarity and significance of results and quality of presentation.

We are pleased to announce that authors of accepted papers from our Workshop are invited to submit an extended version of their work to our special issue in the Journal of Supercomputing.

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