1/ New preprint! The “Socioeconomic Roots of Academic Faculty,” with @alliecmorgan @laberge_nick @DanLarremore Mirta Galesic: osf.io/preprints/soca…

We surveyed 7000 U.S. faculty from 8 disciplines to study how socioeconomic status shapes the academic workforce. 🪴 A summary: 2/ First, we asked faculty about their parents' level of education when they were kids.

Half of faculty reported a parent having a grad degree, and nearly a quarter had a parent with a PhD. That’s 25x the rate of U.S. adults. 😮

Excited to announce our paper "Stacking models for nearly optimal link prediction in complex networks" is now out in @PNASNews 🥳, with @Amir_Ghasemian @HomaHosseinmar1 @aram_galstyan & @eairoldi : pnas.org/content/early/… Here’s a little summary: 1/8 Most networks are incomplete (for reasons). Link prediction algorithms can help fill in the gaps or say which network model fits better. Many link prediction methods exist! But most claim to work well... Is one method the best? How close to optimal predictors are they? 🤔 2/8

Happy 4th birthday to the Index of Complex Networks! 🥳 ICON was launched @netsci2016 and now lists 689 data sets and 5403 networks, spanning all domains of science and all types of networks, in a fully searchable index. icon.colorado.edu/#!/ 1/5 ICON was created rebelliously ✊, to (1) help researchers use a wider variety of empirical networks, (2) quantify the structural breadth of real-world networks, and (3) move us beyond toy models (and orthodoxy), toward a better understanding of network structure and evolution 2/5

Life continues: excited to share a new preprint 🎉 "Denoising large-scale biological data using network filters", with Andrew Kavran, which describes how to leverage networks to filter out independent noise in systems biology data sets [1/4] biorxiv.org/content/10.110… Core idea: in sys bio data, networks tell us which values should (anti-)correlate, and pooling neighbor values can filter out independent noise. This is like image analysis, where networks are the system's "geometry," and community detection is analogous to image segmenting [2/4]

Good models can help us estimate which and how much different factors may influence the covid-19 pandemic's future. This modeling preprint by @StephenKissler @ctedijanto E Goldstein @yhgrad @mlipsitch is 💯. Some key takeaways for me: [1/4]

https://twitter.com/StephenKissler/status/1235943957634396161

1. Data on the two common human betacoronaviruses show evidence of seasonal forcing and some cross-immunity.
2. Given their molecular similarity (they're all betacoronaviruses), we might expect the same for SARS-CoV-2; some statistical evidence from China seems to agree [2/4]

Lewis Fry Richardson (1881-1953) is one of my all-time favorite scholars, and arguably one of the fore-fathers of complex systems. I'm happy to have contributed a chapter revisiting his seminal work on war sizes and timing to a new edited volume about him blogs.prio.org/2019/12/lewis-… Here are some awesome things Richardson did:
1. He discovered fractals, through his work on the Coastline Paradox, which paved the way for (and was cited by) Benoit Mandelbrot’s celebrated work on fractal geometry: wikiwand.com/en/Coastline_p…

Excited to share a new preprint "Stacking models for nearly optimal link prediction in complex networks," led by @Amir_Ghasemian and @HomaHosseinmar1, with @aram_galstyan and @eairoldi: arxiv.org/abs/1909.07578 Here’s a little summary: 1/7 Most networks are incomplete, for reasons, and link prediction can help fill in the gaps. Many such methods exist and most claim to work well. We wondered: Is there one method to rule them all? Are methods better on some domains than others? How close to optimality are they? 2/7

This Fall @CUBoulder, I'm excited to teach a completely new undergrad course called "Biological Networks." For it, I'm writing new lecture notes on network analysis and modeling, which I'll try to share as I finish them. Constructive feedback is welcome. tuvalu.santafe.edu/~aaronc/course… @CUBoulder Lecture 1: Fundamentals of Networks
- What are networks?
- Graph properties of networks
- Four flavors of network analysis and modeling
tuvalu.santafe.edu/~aaronc/course…

I've seen a growing number of computational social science projects using the genderize.io API to assign F/M gender labels to names in research studies. It's an attractive service to use! But, it's not reliable enough for research projects. Here's why: 1/4 The API returns one of {F,M,-} based on the first name, with '-' meaning "don't know." On 500 names sampled randomly from 20,000 ground-truthed faculty names I have, genderize.io guessed correctly only 34% of the time, and it only made a guess on 50% of queries. 2/4

Slides from my Network Science lectures @sfiscience Complex Systems Summer School, Part 1: what are networks and how do we talk about them? (the big picture) tuvalu.santafe.edu/~aaronc/slides… #CSSS19 1/3 Slides from my Network Science lectures @sfiscience Complex Systems Summer School, Part 2: degrees, positions, and communities (plus bonus material) tuvalu.santafe.edu/~aaronc/slides… #CSSS19 2/3

Excited to share this new paper "Productivity, prominence, and the effects of academic environment" with @samfway @alliecmorgan and @DanLarremore, out today in @PNASnews pnas.org/content/early/… Here’s a little summary: 1/7 Previously, we showed how faculty at higher prestige departments tend to be much more productive. But, since faculty in prestigious departments also tend to hire prestigious PhDs, it's unclear which of these—PhD or faculty prestige—drives this higher productivity 2/7

Hypothesis: university prestige shapes the flow of scientists, which determines which scientific ideas get worked on, where. In new work in @epj_ds, we show how this process can create an “epistemic advantage”, w/ @alliecmorgan D. Economou & @samfway epjdatascience.springeropen.com/articles/10.11… 1/5 Prestige predicts who hires whose graduates as faculty (advances.sciencemag.org/content/1/1/e1…), and they carry scientific ideas with them. Via simulation, we show that ideas born at elite universities can spread exponentially further than equally good ideas from less elite places 2/5

New preprint from my group, "Predicting the outcomes of policy diffusion from U.S. states to federal law," with @Nora_Connor. Turns out, both theory and data only poorly predict real policy diffusion, and which/when state-level policies become federal 1/n arxiv.org/abs/1810.08988 But! The timings of state-level adoptions allow for statistical forecasts, with uncertainty, for the timing of a federal policy. We illustrate this for marriage equality and meth precursor laws, and then make real forecasts for recreational marijuana and stand-ur-ground laws 2/n