Hypergraphs, Simplicial Complexes and Graph Representations of Complex Systems with Tina Eliassi-Rad - #547
Research#AI in Network Science📝 Blog|Analyzed: Dec 29, 2025 07:45•
Published: Dec 23, 2021 17:46
•1 min read
•Practical AIAnalysis
This article from Practical AI highlights an interview with Tina Eliassi-Rad, a professor at Northeastern University, focusing on her research at the intersection of network science, complex networks, and machine learning. The discussion centers on how graphs are utilized in her work, differentiating it from standard graph machine learning applications. A key aspect of the interview revolves around her workshop talk, which addresses the challenges in modeling complex systems due to a disconnect from data sourcing and generation. The article suggests a focus on the practical application of AI and the importance of understanding the data's origin for effective modeling.
Key Takeaways
- •The interview explores the use of graphs in complex systems research.
- •It highlights the importance of understanding data sourcing and generation.
- •The discussion differentiates Eliassi-Rad's work from typical graph machine learning.
Reference / Citation
View Original"Tina argues that one of the reasons practitioners have struggled to model complex systems is because of the lack of connection to the data sourcing and generation process."