Dec 23, 2021
Hypergraphs, Simplicial Complexes and Graph Representations of Complex Systems with Tina Eliassi-Rad
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About This Episode
Today we continue our NeurIPS coverage joined by Tina Eliassi-Rad, a professor at Northeastern University, and an invited speaker at the I Still Can’t Believe It’s Not Better! Workshop. In our conversation with Tina, we explore her research at the intersection of network science, complex networks, and machine learning, how graphs are used in her work and how it differs from typical graph machine learning use cases. We also discuss her talk from the workshop, “The Why, How, and When of Representations for Complex Systems”, in which 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. This is definitely a NERD ALERT approved interview!
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Connect with Tina!
Resources
- I Can’t Believe It’s Not Better! (ICBINB) NeurIPS 2021 Workshop
- Paper: The Why, How, and When of Representations for Complex Systems
- Paper: Nonbacktracking Eigenvalues under Node Removal: X-Centrality and Targeted Immunization
- Paper: Selective network discovery via deep reinforcement learning on embedded spaces
- Paper: Understanding the limitations of network online learning
- Paper: Non-backtracking Cycles: Length Spectrum Theory and Graph Mining Applications
- The Data Science Venn Diagram — Drew Conway
- Gauge Equivariant CNNs, Generative Models, and the Future of AI with Max Welling — #267
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Originally published at https://twimlai.com on December 23, 2021.
