{"id":4,"date":"2011-12-08T11:55:34","date_gmt":"2011-12-08T11:55:34","guid":{"rendered":"http:\/\/project.inria.fr\/template1\/?page_id=4"},"modified":"2025-03-24T09:14:15","modified_gmt":"2025-03-24T08:14:15","slug":"home","status":"publish","type":"page","link":"https:\/\/project.inria.fr\/sglm\/","title":{"rendered":"International workshop on Scalable and Deep Graph Learning and Mining (SGLM)"},"content":{"rendered":"
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In conjunction with International Joint Conference on Neural Networks(IJCNN2025)<\/span><\/a>\u00a0Conference, Rome, Italy, June 30-July 5, 2025<\/em><\/h4>\n

SGLM@IJCNN2025<\/strong><\/p>\n<\/header>\n

Call for Papers<\/h2>\n

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.<\/p>\n

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.<\/p>\n

Topics<\/h2>\n

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