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.