Some art that arose from our research was published recently in the collection The Art of Theoretical Biology. Here are some details about our contribution.
Title: Community Matters
Authors: Anna C. F. Lewis, Nick S. Jones, Mason A. Porter, and Charlotte M. Deane
My name is Mason Porter. I am a Professor in the Department of Mathematics at UCLA. Previously I was Professor of Nonlinear and Complex Systems in the Mathematical Institute at University of Oxford. I was also a Tutorial Fellow of Somerville College.
Thursday, April 30, 2020
"A Framework for the Construction of Generative Models for Mesoscale Structure in Multilayer Networks"
We first posted a version of this article (now known as "The Beast") on arXiv in 2016, and (as of today) we are finally completely DONE! Here are some details.
Title: A Framework for the Construction of Generative Models for Mesoscale Structure in Multilayer Networks
Authors: Marya Bazzi, Lucas G. S. Jeub, Alex Arenas, Sam D. Howison, and Mason A. Porter
Software: You can find code for the model, as well as the outputs of the computational experiments in our paper, at this page.
Abstract: Multilayer networks allow one to represent diverse and coupled connectivity patterns—such as time-dependence, multiple subsystems, or both—that arise in many applications and which are difficult or awkward to incorporate into standard network representations. In the study of multilayer networks, it is important to investigate mesoscale (i.e., intermediate-scale) structures, such as dense sets of nodes known as communities, to discover network features that are not apparent at the microscale or the macroscale. The ill-defined nature of mesoscale structure and its ubiquity in empirical networks make it crucial to develop generative models that can produce the features that one encounters in empirical networks. Key purposes of such models include generating synthetic networks with empirical properties of interest, benchmarking mesoscale-detection methods and algorithms, and inferring structure in empirical multilayer networks. In this paper, we introduce a framework for the construction of generative models for mesoscale structures in multilayer networks. Our framework provides a standardized set of generative models, together with an associated set of principles from which they are derived, for studies of mesoscale structures in multilayer networks. It unifies and generalizes many existing models for mesoscale structures in fully ordered (e.g., temporal) and unordered (e.g., multiplex) multilayer networks. One can also use it to construct generative models for mesoscale structures in partially ordered multilayer networks (e.g., networks that are both temporal and multiplex). Our framework has the ability to produce many features of empirical multilayer networks, and it explicitly incorporates a user-specified dependency structure between layers. We discuss the parameters and properties of our framework, and we illustrate examples of its use with benchmark models for community-detection methods and algorithms in multilayer networks.
Title: A Framework for the Construction of Generative Models for Mesoscale Structure in Multilayer Networks
Authors: Marya Bazzi, Lucas G. S. Jeub, Alex Arenas, Sam D. Howison, and Mason A. Porter
Software: You can find code for the model, as well as the outputs of the computational experiments in our paper, at this page.
Abstract: Multilayer networks allow one to represent diverse and coupled connectivity patterns—such as time-dependence, multiple subsystems, or both—that arise in many applications and which are difficult or awkward to incorporate into standard network representations. In the study of multilayer networks, it is important to investigate mesoscale (i.e., intermediate-scale) structures, such as dense sets of nodes known as communities, to discover network features that are not apparent at the microscale or the macroscale. The ill-defined nature of mesoscale structure and its ubiquity in empirical networks make it crucial to develop generative models that can produce the features that one encounters in empirical networks. Key purposes of such models include generating synthetic networks with empirical properties of interest, benchmarking mesoscale-detection methods and algorithms, and inferring structure in empirical multilayer networks. In this paper, we introduce a framework for the construction of generative models for mesoscale structures in multilayer networks. Our framework provides a standardized set of generative models, together with an associated set of principles from which they are derived, for studies of mesoscale structures in multilayer networks. It unifies and generalizes many existing models for mesoscale structures in fully ordered (e.g., temporal) and unordered (e.g., multiplex) multilayer networks. One can also use it to construct generative models for mesoscale structures in partially ordered multilayer networks (e.g., networks that are both temporal and multiplex). Our framework has the ability to produce many features of empirical multilayer networks, and it explicitly incorporates a user-specified dependency structure between layers. We discuss the parameters and properties of our framework, and we illustrate examples of its use with benchmark models for community-detection methods and algorithms in multilayer networks.
Tuesday, April 28, 2020
A Spectacular Ph.D. Thesis Defense by Michelle Feng!
Congratulations to my Ph.D. student Michelle Feng on a superb defense of her thesis!
CONGRATULATIONS to Dr. Michelle Feng (@michellehfeng ) on a successful — and, in fact, spectacular — defense of her Ph.D. thesis, called "Topological Tools for Understanding Complex Systems"!!!!
— Mason Porter (@masonporter) April 28, 2020
You will be hearing a lot from her in the future. pic.twitter.com/gpjueTA6CW
Monday, April 27, 2020
Short Course on "Mathematical and Computational Methods for Complex Social Systems" at the 2021 Joint Mathematics Meetings!
An exciting announcement!
— Mason Porter (@masonporter) April 28, 2020
We (@hzinnbrooks + @michellehfeng + Me + @al_volkening) are organizing an @amermathsoc 'Short Course' on "Mathematical and Computational Methods for Complex Social Systems" at the 2021 Joint Mathematics Meetings (@JointMath)! https://t.co/A4rXWVOWSU pic.twitter.com/e9hTEDLCN4
Sunday, April 26, 2020
"And Then the Dragons Arrived."
I am amused. :)
Let's try this with my own book: "Traditionally, much of the study of networks has focused on structural features. And then the dragons arrived."
— Mason Porter (@masonporter) April 27, 2020
You know, that kind of works. I want to write this book!
This is the book (coauthored with @gleesonj): https://t.co/PtNjiV7voB https://t.co/lZevcObzYt
Saturday, April 25, 2020
Dramatic Readings of Academic Research and Other Scholarly Works
I am doing "dramatic readings" of scholarly research as part of a series of short podcasts for the Annals of Improbable Research (@improbresearch).
— Mason Porter (@masonporter) April 25, 2020
A new one dropped today ("Mask Wiggling"): https://t.co/I9zhPODp1H
Another one ("Cocker’s Arithmetick"): https://t.co/66twU8zjqn
Monday, April 20, 2020
A Paper Called "Lunch Menu" on Google Scholar
I love it!
"Lunch Menu" by T. B. Dishes and P. Kids also exists, cited 14 times https://t.co/8RR1rvYfKs
— Hiroki Sayama (@HirokiSayama) April 21, 2020
Tuesday, April 14, 2020
"A Model for the Influence of Media on the Ideology of Content in Online Social Networks"
One of my papers has just appeared in final form. Here are some details.
Title: A Model for the Influence of Media on the Ideology of Content in Online Social Networks
Authors: Heather Z. Brooks and Mason A. Porter
Abstract: Many people rely on online social networks as sources of news and information, and the spread of media content with ideologies across the political spectrum influences online discussions and impacts offline actions. To examine the impact of media in online social networks, we generalize bounded-confidence models of opinion dynamics by incorporating media accounts as influencers in a network. We quantify partisanship of content with a continuous parameter on an interval, and we formulate higher-dimensional generalizations to incorporate content quality and increasingly nuanced political positions. We simulate our model with one and two ideological dimensions, and we use the results of our simulations to quantify the “entrainment” of content from nonmedia accounts to the ideologies of media accounts in a network. We maximize media impact in a social network by tuning the number of media accounts and the numbers of followers of those accounts. Using numerical computations, we find that the entrainment of the ideology of content that is spread by nonmedia accounts to media ideology depends on a network's structural features, including its size, the mean number of followers of its nodes, and the receptiveness of its nodes to different opinions. We then introduce content quality—a key novel contribution of our work—into our model. We incorporate multiple media sources with ideological biases and quality-level estimates that we draw from real media sources and demonstrate that our model can produce distinct communities (“echo chambers”) that are polarized in both ideology and quality. Our model provides a step toward understanding content quality and ideology in spreading dynamics, with ramifications for how to mitigate the spread of undesired content and promote the spread of desired content.
Title: A Model for the Influence of Media on the Ideology of Content in Online Social Networks
Authors: Heather Z. Brooks and Mason A. Porter
Abstract: Many people rely on online social networks as sources of news and information, and the spread of media content with ideologies across the political spectrum influences online discussions and impacts offline actions. To examine the impact of media in online social networks, we generalize bounded-confidence models of opinion dynamics by incorporating media accounts as influencers in a network. We quantify partisanship of content with a continuous parameter on an interval, and we formulate higher-dimensional generalizations to incorporate content quality and increasingly nuanced political positions. We simulate our model with one and two ideological dimensions, and we use the results of our simulations to quantify the “entrainment” of content from nonmedia accounts to the ideologies of media accounts in a network. We maximize media impact in a social network by tuning the number of media accounts and the numbers of followers of those accounts. Using numerical computations, we find that the entrainment of the ideology of content that is spread by nonmedia accounts to media ideology depends on a network's structural features, including its size, the mean number of followers of its nodes, and the receptiveness of its nodes to different opinions. We then introduce content quality—a key novel contribution of our work—into our model. We incorporate multiple media sources with ideological biases and quality-level estimates that we draw from real media sources and demonstrate that our model can produce distinct communities (“echo chambers”) that are polarized in both ideology and quality. Our model provides a step toward understanding content quality and ideology in spreading dynamics, with ramifications for how to mitigate the spread of undesired content and promote the spread of desired content.
Friday, April 10, 2020
Los Angeles Without Smog
This picture is amazing!
(Tip of the cap to Predrag Cvitanovic.)
Los Angeles with no smog..
— Bill Gross (@Bill_Gross) April 10, 2020
Aerial view of downtown from Silverlake via @alanwdang pic.twitter.com/qOxAAzDzmX
(Tip of the cap to Predrag Cvitanovic.)