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!
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Early Personalized Search
- Tina Eliassi-Rad worked on personalized web searches before GPUs and abundant social data.
- She trained two neural networks to understand user preferences for hyperlinks and web pages.
Analyzing Simulation Data
- At Lawrence Livermore National Lab, Eliassi-Rad analyzed large-scale scientific simulation data, including nuclear explosion simulations.
- This work involved building statistical models over the simulation outcomes.
Graph Representations
- Representing complex systems like the brain with simple graphs fails to capture higher-order relationships.
- Simplicial complexes and hypergraphs offer richer representations but increase computational complexity.