Thursday, April 30, 2020

"Community Matters"

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

"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.

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!

Monday, April 27, 2020

Sunday, April 26, 2020

"And Then the Dragons Arrived."

I am amused. :)

Saturday, April 25, 2020

Dramatic Readings of Academic Research and Other Scholarly Works

Monday, April 20, 2020

A Paper Called "Lunch Menu" on Google Scholar

I love it!



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.