The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Hypergraphs, Simplicial Complexes and Graph Representations of Complex Systems with Tina Eliassi-Rad - #547

Dec 23, 2021

In this engaging conversation, Tina Eliassi-Rad, a Northeastern University professor specializing in network science and machine learning, dives into the intricacies of graph representations in complex systems. She highlights the challenges of accurately modeling epidemics and the implications of asymmetric information in economic networks. Tina also discusses her workshop talk, emphasizing the disconnect between data sourcing and modeling practices. With insights on graph theory and network interventions, this discussion is a treasure trove for AI enthusiasts!

35:40

Creator website

Episode guests

Tina Eliassi-Rad

AI Chapters

Episode notes

Intro

2min

Navigating Network Science and Machine Learning

21min

Exploring the Length Spectrum and Graph Similarity

2min

Exploring Graph Theory and Epidemic Modeling in Complex Networks

3min

Analyzing Network Interventions and Properties

2min

Understanding Phenomena in Data Science: Insights on Predictions and Modeling

5min

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!

The complete show notes for this episode can be found at twimlai.com/go/547