I am here to help. pic.twitter.com/JBQbTAPTQX
— Mason Porter (@masonporter) June 17, 2020
9 hours ago
Thursday, June 18, 2020
"I Am Here to Help"
My tweet went viral.
Wednesday, June 17, 2020
Congratulations to the 2020 Graduates from Our Research Group! (Humans First; Mathematicians Second)
We had our commencement ceremony.
CONGRATULATIONS to the Class of 2020 from the research group!
And thanks to Prof. Chad Topaz for a wonderful commencement speech (one of the best I ever heard and so meaningful to me). Thanks to the guests who joined us.
The graduating class of 2020:
Postdoc: Heather Zinn Brooks
Undergrads: Qinyi Chen, Unchitta Kanjanasaratool, Tony Liu
PhD students: Michelle Feng, Yacoub Kureh, William Oakley
#truecolors
Here are a couple of pictures.
And here is the text of Chad's speech.
"Are mathematicians human?"
I was very unsure of how to begin this speech, and so I did the natural thing: I Googled “graduation speech opening” and I found a site with the following advice, which I am going to obey.
1. Offer formal words recognizing the honored guests.
I hereby decree that this honored group seems familiar; have we met before?
2. Use humor. If you are confident that your humor will work, making everyone laugh will be a great start.
I am confident. Please go ahead and laugh now.
3. Enthusiastically congratulate graduates on their success.
From my heart, congratulations.
4. State the topic of your speech.
Those of you who know me know that I often go against the crowd. Some graduation speech themes are all too common, so I’ll be avoiding the following messages in this speech:
- Challenges are opportunities
- Be yourself
- The world is your oyster
- Love will triumph over all
- With great power comes great responsibility
So, for this speech, I have chosen the topic “Are mathematicians human?"
I was a postdoctoral fellow in this department from 2003 - 2006. During the last year of my postdoc, and wanting desperately to stay in academia, I applied for tenure track jobs. While I was lucky to get a couple of offers, none of the offers would have resolved my dual career couple issues. It seemed reasonable that I'd have a better chance of finding work in Los Angeles than my husband would in rural Maine. So I made one of the hardest decisions of my life: I stayed in Los Angeles and, in doing so, quit being a mathematician. But not before, in a moment of extreme frustration and angst, I committed the only physically violent act of my adult life by hurling a bowl of oatmeal at the wall of my apartment.
Sometimes we don't realize what part of our identity means until we don't have it anymore. I didn't see this coming, but when I could no longer call myself a mathematician, or at least not professionally, I wasn't sure what I was anymore.
The next year of my life began fairly miserably. I worked as a college administrator on another campus in a unit that was a poor fit for me. I had few personal goals other than feeling sorry myself. Gradually, though, things turned around. I slowly built stronger relationships with friends. I got involved in service work related to issues I cared about. I rediscovered how much I loved playing chamber works with other musicians. I reconnected with all of the parts of myself that I had let shrink.
I stopped identifying principally as a mathematician and started identifying principally as a human.
But what makes us human? Characteristics and abilities once thought to distinguish us from other animals -- the use of tools, the ability to recognize ourselves, the size of our brains, and much more -- turn out to be found in various corners of the animal kingdom. Some evidence from the natural sciences, though, does suggest what makes humans unique.
First, we are human because of the degree to which we are wired to help each other. In psychology experiments, children as young as 14 months will spontaneously help a person who is struggling or who looks worried or who drops an item. At age two years, children will help someone who isn't even aware of their own need for help, say, because they didn't realize they had dropped an item. And rewards don't seem to play a role. In experiments on 20 month old toddlers, those who had previously received rewards for being helpful acted equally helpfully as a control group that hadn't received rewards. Other species certainly have been observed to engage in helping behavior, but within different parameters. For instance, close by on the evolutionary tree, chimpanzees will share food. Chimps, however, appear to be far more selective about their helping behavior, sharing only with close relatives or potential mates. In short, humans appear to be wired to be indiscriminately cooperative.
Second, we are human because of our ability to imagine and know things beyond our senses. Take the famous psychology experiment called the Sally-Anne task. In this task, there are two dolls named Sally and Anne. A young child, who is the subject of the experiment, sees Sally putting a marble in a basket while Anne watches. Then Sally leaves the room. While she's gone, Anne removes the marble from the basket and puts it inside a box. Then Sally comes back into the room. The experimenter asks the child where Sally will look for the marble, most children answer that Sally will look in the basket, where she had originally left it. The child knows that the marble is not there, but understands that Sally will have a different thought. Let me emphasize this: it's not merely that the child knows that the marble moved. The child can put themself in the mindset of another person and imagine what that person thinks. On the other hand, in a version of the experiment designed to assess chimpanzees, the chimps generally failed the test.
So two possible answers to "what makes us human" are our level of radical, selfless cooperation and our capacity to put ourselves in the shoes of another, a quality we sometimes refer to as empathy.
This speech would not be authentic if I did not call out that we are living during challenging times. Now let's be real: epidemic disease has been with humankind for a long time, with the earliest records of an influenza-like epidemic coming from central and Southern Asia around 1200 BC. Racial injustice has been with us in the United States since before we even WERE the United States. Still, we seem to be in an especially challenging moment right now, with bungled public health efforts and the continued killing of Black people by the police. It can be hard to believe that cooperation and empathy are our nature. So I take solace in the aforementioned scientific evidence and I say thank goodness for lab experiments.
It's not just on the national and international stages that the better parts of human nature can be obscured. You will have moments in your professional and personal lives when you will have the opportunity to put your humanity second to more immediate or more tangible or just plan easier ends. My message to you today is simple: always, first and foremost, be a human.
I asked Mason to give me a few very brief words about the human qualities of each of you, graduates. Mason respected my request... except for the brevity part! So please know that when I mention each of you, the brevity is mine, not his.
Heather, you are a valued and highly respected source of wisdom.
Michelle, your creativity and passion are inspirational.
Yacoub, you are driven to help, from each individual student up to saving the world.
Will, your good cheer powers not only your own efforts, but lifts those around you.
Quinyi, you have a rare blend of determination and humility.
Unchitta, you have limitless compassion.
Tony, you fearlessly reach out to those in need.
By the way, while today is mostly about you, graduates, it is also a little bit about Mason. It doesn't escape my notice that your advisor displays stellar human qualities as well, which is perhaps why we are drawn to him.
To the seven honored and accomplished celebrants, I wish you hearty congratulations and all the best for the future. Thanks to your hard work and dedication, you are outstanding students, scholars, teachers, mathematicians, thinkers. But most of all, you are outstanding humans.
CONGRATULATIONS to the Class of 2020 from the research group!
And thanks to Prof. Chad Topaz for a wonderful commencement speech (one of the best I ever heard and so meaningful to me). Thanks to the guests who joined us.
The graduating class of 2020:
Postdoc: Heather Zinn Brooks
Undergrads: Qinyi Chen, Unchitta Kanjanasaratool, Tony Liu
PhD students: Michelle Feng, Yacoub Kureh, William Oakley
#truecolors
Here are a couple of pictures.
And here is the text of Chad's speech.
"Are mathematicians human?"
I was very unsure of how to begin this speech, and so I did the natural thing: I Googled “graduation speech opening” and I found a site with the following advice, which I am going to obey.
1. Offer formal words recognizing the honored guests.
I hereby decree that this honored group seems familiar; have we met before?
2. Use humor. If you are confident that your humor will work, making everyone laugh will be a great start.
I am confident. Please go ahead and laugh now.
3. Enthusiastically congratulate graduates on their success.
From my heart, congratulations.
4. State the topic of your speech.
Those of you who know me know that I often go against the crowd. Some graduation speech themes are all too common, so I’ll be avoiding the following messages in this speech:
- Challenges are opportunities
- Be yourself
- The world is your oyster
- Love will triumph over all
- With great power comes great responsibility
So, for this speech, I have chosen the topic “Are mathematicians human?"
I was a postdoctoral fellow in this department from 2003 - 2006. During the last year of my postdoc, and wanting desperately to stay in academia, I applied for tenure track jobs. While I was lucky to get a couple of offers, none of the offers would have resolved my dual career couple issues. It seemed reasonable that I'd have a better chance of finding work in Los Angeles than my husband would in rural Maine. So I made one of the hardest decisions of my life: I stayed in Los Angeles and, in doing so, quit being a mathematician. But not before, in a moment of extreme frustration and angst, I committed the only physically violent act of my adult life by hurling a bowl of oatmeal at the wall of my apartment.
Sometimes we don't realize what part of our identity means until we don't have it anymore. I didn't see this coming, but when I could no longer call myself a mathematician, or at least not professionally, I wasn't sure what I was anymore.
The next year of my life began fairly miserably. I worked as a college administrator on another campus in a unit that was a poor fit for me. I had few personal goals other than feeling sorry myself. Gradually, though, things turned around. I slowly built stronger relationships with friends. I got involved in service work related to issues I cared about. I rediscovered how much I loved playing chamber works with other musicians. I reconnected with all of the parts of myself that I had let shrink.
I stopped identifying principally as a mathematician and started identifying principally as a human.
But what makes us human? Characteristics and abilities once thought to distinguish us from other animals -- the use of tools, the ability to recognize ourselves, the size of our brains, and much more -- turn out to be found in various corners of the animal kingdom. Some evidence from the natural sciences, though, does suggest what makes humans unique.
First, we are human because of the degree to which we are wired to help each other. In psychology experiments, children as young as 14 months will spontaneously help a person who is struggling or who looks worried or who drops an item. At age two years, children will help someone who isn't even aware of their own need for help, say, because they didn't realize they had dropped an item. And rewards don't seem to play a role. In experiments on 20 month old toddlers, those who had previously received rewards for being helpful acted equally helpfully as a control group that hadn't received rewards. Other species certainly have been observed to engage in helping behavior, but within different parameters. For instance, close by on the evolutionary tree, chimpanzees will share food. Chimps, however, appear to be far more selective about their helping behavior, sharing only with close relatives or potential mates. In short, humans appear to be wired to be indiscriminately cooperative.
Second, we are human because of our ability to imagine and know things beyond our senses. Take the famous psychology experiment called the Sally-Anne task. In this task, there are two dolls named Sally and Anne. A young child, who is the subject of the experiment, sees Sally putting a marble in a basket while Anne watches. Then Sally leaves the room. While she's gone, Anne removes the marble from the basket and puts it inside a box. Then Sally comes back into the room. The experimenter asks the child where Sally will look for the marble, most children answer that Sally will look in the basket, where she had originally left it. The child knows that the marble is not there, but understands that Sally will have a different thought. Let me emphasize this: it's not merely that the child knows that the marble moved. The child can put themself in the mindset of another person and imagine what that person thinks. On the other hand, in a version of the experiment designed to assess chimpanzees, the chimps generally failed the test.
So two possible answers to "what makes us human" are our level of radical, selfless cooperation and our capacity to put ourselves in the shoes of another, a quality we sometimes refer to as empathy.
This speech would not be authentic if I did not call out that we are living during challenging times. Now let's be real: epidemic disease has been with humankind for a long time, with the earliest records of an influenza-like epidemic coming from central and Southern Asia around 1200 BC. Racial injustice has been with us in the United States since before we even WERE the United States. Still, we seem to be in an especially challenging moment right now, with bungled public health efforts and the continued killing of Black people by the police. It can be hard to believe that cooperation and empathy are our nature. So I take solace in the aforementioned scientific evidence and I say thank goodness for lab experiments.
It's not just on the national and international stages that the better parts of human nature can be obscured. You will have moments in your professional and personal lives when you will have the opportunity to put your humanity second to more immediate or more tangible or just plan easier ends. My message to you today is simple: always, first and foremost, be a human.
I asked Mason to give me a few very brief words about the human qualities of each of you, graduates. Mason respected my request... except for the brevity part! So please know that when I mention each of you, the brevity is mine, not his.
Heather, you are a valued and highly respected source of wisdom.
Michelle, your creativity and passion are inspirational.
Yacoub, you are driven to help, from each individual student up to saving the world.
Will, your good cheer powers not only your own efforts, but lifts those around you.
Quinyi, you have a rare blend of determination and humility.
Unchitta, you have limitless compassion.
Tony, you fearlessly reach out to those in need.
By the way, while today is mostly about you, graduates, it is also a little bit about Mason. It doesn't escape my notice that your advisor displays stellar human qualities as well, which is perhaps why we are drawn to him.
To the seven honored and accomplished celebrants, I wish you hearty congratulations and all the best for the future. Thanks to your hard work and dedication, you are outstanding students, scholars, teachers, mathematicians, thinkers. But most of all, you are outstanding humans.
Tuesday, June 09, 2020
"Spatial Strength Centrality and the Effect of Spatial Embeddings on Network Architecture"
One of my papers came out in final form today. Here is a link to the paper, and here are some details.
Title: "Spatial Strength Centrality and the Effect of Spatial Embeddings on Network Architecture"
Authors: Andrew Liu and Mason A. Porter
Abstract: For many networks, it is useful to think of their nodes as being embedded in a latent space, and such embeddings can affect the probabilities for nodes to be adjacent to each other. In this paper, we extend existing models of synthetic networks to spatial network models by first embedding nodes in Euclidean space and then modifying the models so that progressively longer edges occur with progressively smaller probabilities. We start by extending a geographical fitness model by employing Gaussian-distributed fitnesses, and we then develop spatial versions of preferential attachment and configuration models. We define a notion of “spatial strength centrality” to help characterize how strongly a spatial embedding affects network structure, and we examine spatial strength centrality on a variety of real and synthetic networks.
Title: "Spatial Strength Centrality and the Effect of Spatial Embeddings on Network Architecture"
Authors: Andrew Liu and Mason A. Porter
Abstract: For many networks, it is useful to think of their nodes as being embedded in a latent space, and such embeddings can affect the probabilities for nodes to be adjacent to each other. In this paper, we extend existing models of synthetic networks to spatial network models by first embedding nodes in Euclidean space and then modifying the models so that progressively longer edges occur with progressively smaller probabilities. We start by extending a geographical fitness model by employing Gaussian-distributed fitnesses, and we then develop spatial versions of preferential attachment and configuration models. We define a notion of “spatial strength centrality” to help characterize how strongly a spatial embedding affects network structure, and we examine spatial strength centrality on a variety of real and synthetic networks.
Sunday, June 07, 2020
The COVID-19 Graduating Class
I bought one of these for each graduating member of my research group. pic.twitter.com/H8W7Dxg2aF
— Mason Porter (@masonporter) June 7, 2020
Thursday, June 04, 2020
A Great Ph.D. Defense by Dr. Will Oakley
Congratulations to my Ph.D. student Will Oakley on an excellent defense of his thesis!
Congratulations to Dr. Will Oakley for a successful defense of his Ph.D. dissertation, titled "Strong Consensus-Seeking in a Model of Social Consensus Formation"! pic.twitter.com/SxCEOkMpdr
— Mason Porter (@masonporter) June 4, 2020
Tuesday, June 02, 2020
"Fitting in and Breaking Up: A Nonlinear Version of Coevolving Voter Models"
A paper of mine came out in final form today. Here are some details.
Title: Fitting in and Breaking Up: A Nonlinear Version of Coevolving Voter Models
Authors: Yacoub H. Kureh and Mason A. Porter
Abstract: We investigate a nonlinear version of coevolving voter models, in which node states and network structure update as a coupled stochastic process. Most prior work on coevolving voter models has focused on linear update rules with fixed and homogeneous rewiring and adopting probabilities. By contrast, in our nonlinear version, the probability that a node rewires or adopts is a function of how well it “fits in” with the nodes in its neighborhood. To explore this idea, we incorporate a local-survey parameter σ_i that encodes the fraction of neighbors of an updating node i that share its opinion state. In an update, with probability σ^q_i(for some nonlinearity parameter q), the updating node rewires; with complementary probability 1 − σ^q_i, the updating node adopts a new opinion state. We study this mechanism using three rewiring schemes: after an updating node deletes one of its discordant edges, it then either (1) “rewires-to-random” by choosing a new neighbor in a random process; (2) “rewires-to-same” by choosing a new neighbor in a random process from nodes that share its state; or (3) “rewires-to-none” by not rewiring at all (akin to “unfriending” on social media). We compar eour nonlinear coevolving voter model to several existing linear coevolving voter models on various network architectures. Relative to those models, we find in our model that initial network topology plays a larger role in the dynamics and that the choice of rewiring mechanism plays a smaller role. A particularly interesting feature of our model is that, under certain conditions, the opinion state that is held initially by a minority of the nodes can effectively spread to almost every node in a network if the minority nodes view themselves as the majority. In light of this observation, we relate our results to recent work on the majority illusion in social networks.
Title: Fitting in and Breaking Up: A Nonlinear Version of Coevolving Voter Models
Authors: Yacoub H. Kureh and Mason A. Porter
Abstract: We investigate a nonlinear version of coevolving voter models, in which node states and network structure update as a coupled stochastic process. Most prior work on coevolving voter models has focused on linear update rules with fixed and homogeneous rewiring and adopting probabilities. By contrast, in our nonlinear version, the probability that a node rewires or adopts is a function of how well it “fits in” with the nodes in its neighborhood. To explore this idea, we incorporate a local-survey parameter σ_i that encodes the fraction of neighbors of an updating node i that share its opinion state. In an update, with probability σ^q_i(for some nonlinearity parameter q), the updating node rewires; with complementary probability 1 − σ^q_i, the updating node adopts a new opinion state. We study this mechanism using three rewiring schemes: after an updating node deletes one of its discordant edges, it then either (1) “rewires-to-random” by choosing a new neighbor in a random process; (2) “rewires-to-same” by choosing a new neighbor in a random process from nodes that share its state; or (3) “rewires-to-none” by not rewiring at all (akin to “unfriending” on social media). We compar eour nonlinear coevolving voter model to several existing linear coevolving voter models on various network architectures. Relative to those models, we find in our model that initial network topology plays a larger role in the dynamics and that the choice of rewiring mechanism plays a smaller role. A particularly interesting feature of our model is that, under certain conditions, the opinion state that is held initially by a minority of the nodes can effectively spread to almost every node in a network if the minority nodes view themselves as the majority. In light of this observation, we relate our results to recent work on the majority illusion in social networks.
Saturday, May 30, 2020
"Nonlinearity + Networks: A 2020 Vision"
Here is the final, published version of my forward-looking book chapter about where network science and some directions that it is heading. It appears in the book Emerging Frontiers in Nonlinear Science.
Title: Nonlinearity + Networks: A 2020 Vision
Abstract: I briefly survey several fascinating topics in networks and nonlinearity. I highlight a few methods and ideas, including several of personal interest, that I anticipate to be especially important during the next several years. These topics include temporal networks (in which a network’s entities and/or their interactions change in time), stochastic and deterministic dynamical processes on networks, adaptive networks (in which a dynamical process on a network is coupled to dynamics of network structure), and network structure and dynamics that include “higher-order” interactions (which involve three or more entities in a network). I draw examples from a variety of scenarios, including contagion dynamics, opinion models, waves, and coupled oscillators.
And in case you want to help spread the word, here is my recent tweet.
Title: Nonlinearity + Networks: A 2020 Vision
Abstract: I briefly survey several fascinating topics in networks and nonlinearity. I highlight a few methods and ideas, including several of personal interest, that I anticipate to be especially important during the next several years. These topics include temporal networks (in which a network’s entities and/or their interactions change in time), stochastic and deterministic dynamical processes on networks, adaptive networks (in which a dynamical process on a network is coupled to dynamics of network structure), and network structure and dynamics that include “higher-order” interactions (which involve three or more entities in a network). I draw examples from a variety of scenarios, including contagion dynamics, opinion models, waves, and coupled oscillators.
And in case you want to help spread the word, here is my recent tweet.
Here's the final, published version of my forward-looking chapter, "Nonlinearity + Networks: A 2020 Vision": https://t.co/eLdlD45oe8
— Mason Porter (@masonporter) May 30, 2020
It appears in the book "Emerging Frontiers in Nonlinear Science" (https://t.co/IBGX7bMyeA)
Friday, May 29, 2020
Topography Will Tear Us Apart
I feel like this tweet was written just for me.
(Tip of the cap to Dave Richeson.)
Unknown Topographies https://t.co/uyNZF9G7TX
— Brian Glaser (@bsglaser) May 29, 2020
(Tip of the cap to Dave Richeson.)
Thursday, May 28, 2020
An Awesome Ph.D. Defense by Dr. Yacoub Kureh!
Congratulations to my Ph.D. student Yacoub Kureh on an awesome defense of his thesis!
CONGRATULATIONS to Dr. Yacoub Kureh (@Yacoub_Kureh) on a successful and very impressive defense of his PhD thesis, called "Nonlinear opinion models and other networked systems"!!! pic.twitter.com/fXDNIE1GRX
— Mason Porter (@masonporter) May 28, 2020
Tuesday, May 26, 2020
Starting Out the Hard Way
I was the TA in the first course that Alex Vladimirsky (now the Director of the PhD program of my graduate alma mater) was ever in charge of.
Two decades later, I am still the most opinionated TA that he's ever had with respect to how to teach a course. People should get the most difficult person out of the way at the beginning, right?
Two decades later, I am still the most opinionated TA that he's ever had with respect to how to teach a course. People should get the most difficult person out of the way at the beginning, right?
He confirmed this and found out about the ducks at our meeting this morning! It’s perfect bc he tells me that when I need a debugging break, I should talk with a rubber duck, so I think the rubber duck that is living in the tex doc right now has a permanent home 😊 (for now)
— Mallory Gaspard (@mallorygaspard) May 27, 2020
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
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.
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.)
Sunday, March 29, 2020
RIP Philip Anderson (1923–2020)
The extraordinary condensed-matter physicist Philip Anderson died today.
I can't yet find an obituary to include as a hyperlink.
Update (3/30/20): The New York Times has published an obituary.
Update (3/30/20): Here is an obituary from Princeton University.
I can't yet find an obituary to include as a hyperlink.
Update (3/30/20): The New York Times has published an obituary.
Update (3/30/20): Here is an obituary from Princeton University.
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