Mason Porter on Community Detection and Data Topology

Episode Notes

One way of looking at the world reveals it as an interference pattern of dynamic, ever-changing links — relationships that grow and break in nested groups of multilayer networks. Identity can be defined by informational exchange between one cluster of relationships and any other. A kind of music starts to make itself apparent in the avalanche of data and new analytical approaches that a century of innovation has availed us. But just as with new music genres, it requires a trained ear to attune to unfamiliar order…what can we learn from network science and related general, abstract mathematical approaches to discovering this order in a flood of numbers?

Welcome to COMPLEXITY, the official podcast of the Santa Fe Institute. I’m your host, Michael Garfield, and in every episode we bring you with us for far-ranging conversations with our worldwide network of rigorous researchers developing new frameworks to explain the deepest mysteries of the universe.

This week we speak with SFI External Professor, UCLA mathematician Mason Porter (UCLA WebsiteTwitterGoogle ScholarWikipedia), about his research on community detection in networks and the topology of data — going deep into a varied toolkit of approaches that help scientists disclose deep structures in the massive data-sets produced by modern life.

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I know it comes as a surprise, but this is our penultimate episode.  Please stay tuned for one more show in May when SFI President David Krakauer and I will reflect on major themes and highlights from the last three-and-a-half years, and look forward to what I’ll be doing next! It’s been an honor and a pleasure to bring complex systems science to you in this way, and hope we stay in touch. I won’t be hard to find.

Thank you for listening.

Podcast theme music by Mitch Mignano.

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Mentioned & Related Media:

Bounded Confidence Models of Opinion Dynamics on Networks
SFI Seminar by Mason Porter (live Twitter coverage & YouTube stream recording)

Communities in Networks
by Mason Porter, Jukka-Pekka Onnela, & Peter Mucha

Social Structure of Facebook Networks
by Amanda Traud, Peter Mucha, & Mason Porter

Critical Truths About Power Laws
by Michael Stumpf & Mason Porter

The topology of data
by Mason Porter, Michelle Feng, & Eleni Katifori

Complex networks with complex weights
by Lucas Böttcher & Mason A. Porter

A Bounded-Confidence Model of Opinion Dynamics on Hypergraphs
by Abigail Hicock, Yacoub Kureh, Heather Z. Brooks, Michelle Feng, & Mason Porter

A multilayer network model of the coevolution of the spread of a disease and competing opinions
by Kaiyan Peng, Zheng Lu, Vanessa Lin, Michael Lindstrom, Christian Parkinson, Chuntian Wang, Andrea Bertozzi, & Mason Porter

Social network analysis for social neuroscientists
Elisa C Baek, Mason A Porter, & Carolyn Parkinson

Community structure in social and biological networks
by Michelle Girvan & Mark Newman

The information theory of individuality
by David Krakauer, Nils Bertschinger, Eckehard Olbrich, Jessica C Flack, Nihat Ay

Social capital I: measurement and associations with economic mobility
by Raj Chetty, Matthew O. Jackson, Theresa Kuchler, Johannes Stroebel, Nathaniel Hendren, Robert B. Fluegge, Sara Gong, Federico Gonzalez, Armelle Grondin, Matthew Jacob, Drew Johnston, Martin Koenen, Eduardo Laguna-Muggenburg, Florian Mudekereza, Tom Rutter, Nicolaj Thor, Wilbur Townsend, Ruby Zhang, Mike Bailey, Pablo Barberá, Monica Bhole & Nils Wernerfelt 

Hierarchical structure and the prediction of missing links in networks
by Aaron Clauset, Cristopher Moore, M.E.J. Newman

Gregory Bateson (Wikipedia)

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“Why Do We Sleep?”
by Van Savage & Geoffrey West at Aeon Magazine

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