In today's rapidly evolving technological landscape, we confront the intricate challenges of complex systems head-on. Graph-based modeling often fails to capture the inherent complexities of such systems and then we move on to a diverse array of complex graph structures: knowledge graphs, attributed graphs, multilayer graphs, hypergraphs, and more. These structures provide more accurate representations for these intricate systems.
In the midst of this complexity, the importance of trustworthy AI, particularly in foundational model research, cannot be overstated. Ensuring ethical, explainable, and fair AI aligns perfectly with the nuances of complex systems. Trustworthy AI hinges on our ability to understand and make transparent AI algorithms that grapple with intricate interactions within these systems. Simultaneously, the reliability of foundation models plays a pivotal role in various AI applications reliant on complex graph-based data.
This workshop aims to bring researchers from these diverse but related fields together and embark interesting discussions on new challenging applications that require complex system modeling and discovering ingenious reasoning methods. We have invited several distinguished speakers with their research interest spanning from the theoretical to experimental aspects of complex networks.
We invite submissions from participants who can contribute to the theory and applications of modeling complex graph structures such as hypergraphs, multilayer networks, knowledge graphs, etc. The topics of interest include, but not limited to, the following:
Graph Foundation Models
Fairness-aware Learning in Multilayer Graphs
Benchmarking Foundation Models with Complex Data
Privacy Preservation in Complex Graphs
Causal Inference and Complex Networks
Knowledge Graph-enhanced Foundation Models
Theoretical analysis of graph algorithms or models
Network representation learning and manifold embedding methods
Optimization methods for graphs/manifolds
Link analysis/prediction, node classification, clustering for complex graph structures
Probabilistic and graphical models for structured data
Knowledge graph construction
Social network analysis and measures
Constraint satisfaction and programming (CP), (inductive) logic programming (LP and ILP)
Papers will be presented in poster format and some will be selected for oral presentation. Through invited talks and presentations by the participants, this workshop will bring together current advances in Network Science as well as Machine Learning, and set the stage for continuing interdisciplinary research discussions.