Aaron Clauset

'Pedigree is not destiny' when it comes to scholarly success

Anonymous — Wed, 01 May 2019 17:46:12 +0000

'Pedigree is not destiny' when it comes to scholarly success Anonymous (not verified) Wed, 05/01/2019 - 11:46

Santa Fe Institute

What matters more to a scientist’s career success: where they currently work, or where they got their Ph.D.? It’s a question a team of researchers teases apart in a new paper published in PNAS. Their analysis calls into question a common assumption underlying academia: that a researcher’s productivity reflects their scientific skill, which is reflected in the prestige of their doctoral training.

It’s true that faculty at prestigious universities publish more scientific papers and receive more citations and awards than professors at lower-ranked institutions. It’s also true that prestigious schools tend to hire new faculty who hold Ph.D.s from similarly prestigious programs. But according to the authors of the new study, an early career researcher’s current working environment is a better predictor of their future success than is the prestige of their doctoral training.

“Pedigree is not destiny,” says SFI External Professor Aaron Clauset (CU ��«��Ƶ), a co-author on the paper. “Our analysis supports the fairly radical idea for academia that where you train doesn’t directly impact your future productivity.”

The team looked at two basic measures of academic success — productivity (how many papers a researcher publishes) and prominence (how often their work is cited) — of 2453 tenure-track faculty in all 205 Ph.D.-granting computer science departments in the US and Canada during the five years before and five years following those individual’s first faculty appointment.

“We wanted to disentangle the impact of environment on productivity and prominence, and to isolate the effects of where someone trained versus where they went on to work as faculty,” says lead author Samuel Way (CU ��«��Ƶ). “On the prominence side, people do retain some benefit from having studied in a prestigious Ph.D. program. They continue to accumulate citations from their doctoral work.”

But the prestige of the training program seems to play little role in how many papers researchers go on to produce once they begin their appointments in a new place. “Someone like me, who trained at Colorado, and someone from MIT… if we both end up at Stanford, our productivity will look the same,” says Way.

The authors identify several possible mechanisms driving the increased productivity of faculty at more prestigious institutions. Selection criteria in hiring, expectations for high productivity once hired, and selective retention of productive faculty were all considered. “We only find weak evidence for each,” says Way. However, the prestige of the current work environment had a strong effect on productivity.

Identifying the underlying “forces that tilt the scientific playing field in favor of some scientists over others,” as Clauset says, is important for identifying and potentially correcting the systemic biases that may be limiting the production of scientific knowledge.

“…our findings have direct implications for research on the science of science, which often assumes, implicitly if not explicitly, that meritocratic principles or mechanisms govern the production of knowledge,” write the authors. “Theories and models that fail to account for the environmental mechanism identified here, and the more general causal effects of prestige on productivity and prominence, will thus be incomplete.”

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Environmental Changes and the Dynamics of Musical Identity

Anonymous — Tue, 09 Apr 2019 19:08:10 +0000

Environmental Changes and the Dynamics of Musical Identity Anonymous (not verified) Tue, 04/09/2019 - 13:08

Aaron Clauset and Sam Way

Musical tastes reflect our unique values and experiences, our relationships with others, and the places where we live. But as each of these things changes, do our tastes also change to reflect the present, or remain fixed, reflecting our past? Here, we investigate how where a person lives shapes their musical preferences, using geographic relocation to construct quasi-natural experiments that measure short- and long-term effects. Analyzing comprehensive data on over 16 million users on Spotify, we show that relocation within the United States has only a small impact on individuals’ tastes, which remain more similar to those of their past environments. We then show that the age gap between a person and the music they consume indicates that adolescence, and likely their environment during these years, shapes their lifelong musical tastes. Our results demonstrate the robustness of individuals’ musical identity, and shed new light on the development of preferences.

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Do all networks obey the scale-free law? Maybe not

Anonymous — Mon, 04 Mar 2019 23:33:48 +0000

Do all networks obey the scale-free law? Maybe not Anonymous (not verified) Mon, 03/04/2019 - 16:33

Daniel Strain

As Benjamin Franklin once joked, death and taxes are universal. Scale-free networks may not be, at least according to a new study from CU ��«��Ƶ.

The research challenges a popular two-decade-old theory that networks of all kinds, from Facebook and Twitter to the interactions of genes in yeast cells, follow a common architecture that mathematicians call “scale-free.”

Such networks fit into a larger category of networks that are dominated by a few hubs with many more connections than the vast majority of nodes—think Twitter where for every Justin Bieber (105 million followers) and Kim Kardashian (60 million followers) out there, you can find thousands of users with just a handful of fans.

Key takeaways

A popular theory claims that all networks are “scale-free”—meaning that the patterns of connections coming into and out of nodes follows a precise mathematical structure called a power law distribution.
CU ��«��Ƶ researchers set out to test that idea, analyzing more than 900 networks from the realms of biology, technology, transportation and more.
They found that only about 4 percent of networks met the strictest definition for being scale-free—and close to half didn’t fit the bill at all.

In research published this week in the journal Nature Communications, CU ��«��Ƶ’s Anna Broido and Aaron Clauset set out to test that trendy theory. They used computational tools to analyze a huge dataset of more than 900 networks, with examples from the realms of biology, transportation, technology and more.

Their results suggest that death and taxes may not have much competition, at least in networks. Based on Broido and Clauset’s analysis, close to 50 percent of real networks didn’t meet even the most liberal definition of what makes a network scale-free.

Those findings matter, Broido said, because the shape of a network determines a lot about its properties, including how susceptible it is to targeted attacks or disease outbreaks.

“It’s important to be careful and precise in defining things like what it means to be a scale-free network,” said Broido, a graduate student in the Department of Applied Mathematics.

Clauset, an associate professor in the Department of Computer Science and the BioFrontiers Institute, agrees.

“The idea of scale-free networks has been a unifying but controversial theme in network theory for nearly 20 years,” he said. “Resolving the controversy has been difficult because we lacked good tools and broad data. What we’ve found now is that there is little evidence for classically scale-free networks except in a few specific places. Most networks don’t look scale-free at all.”

Power law

Deciding whether or not a network is “scale-free,” however, can be tricky. Many types of networks look similar from a distance.

But Scale-free networks are special because the patterns of connections coming into and out of nodes follows a precise mathematical form called a power law distribution.

“If human height followed a power law, you might expect one person to be as tall as the Empire State Building, 10,000 people to be as tall as a giraffe, and more than 150 million to be only about 7-inches-tall,” Clauset said.

Beginning in the late 1990s, a handful of researchers made a bold claim that all real-world networks follow a universal structure represented by such giraffe- and inch-sized disparities.

There was just one problem: “The original claims were mostly based on analyzing a handful of networks with very rough tools,” Clauset said. “The idea was provocative, but also, in retrospect, quite speculative.”

To take scale-free networks out of the realm of speculation, he and Broido turned to the Index of Complex Networks (ICON). This archive, which was assembled by Clauset’s research group at CU ��«��Ƶ, lists data on thousands of networks from every scientific domain. They include the social links between Star Wars characters, interactions among yeast proteins, friendships on Facebook and Twitter, airplane travel and more.

Their findings were stark. By applying a series of statistical tests of increasing severity, the researchers calculated that only about 4 percent of the networks they studied met the strictest criteria for being scale free, meaning the number of connections that each node carried followed a power-law distribution. These special networks included some types of protein networks in cells and certain kinds of technological networks.

A multitude of shapes

But not all researchers use those exact requirements to decide what makes a scale-free network, Broido said. To account for these alternative definitions, she and Clauset adapted their tests to account for each of the variations.

“Wherever you’re coming from, one of our definitions should be close to what you’re thinking,” Broido said.

Despite the added flexibility, most networks still failed to show evidence even for weakly scale-free structure. Roughly half of all biological networks and all social networks, for example, didn’t look like anything close to a scale-free network, no matter how flexible the definitions were made.

Far from being a let-down, Clauset sees these null findings in a positive light: if scale-free isn’t the norm, then scientists are free to explore new and more accurate structures for the networks people encounter every day.

“The diversity of real networks presents a mystery,” he said. “What are the common shapes of the networks? How do different kinds of networks assemble and maintain their structure over time? I’m excited that our findings open up room to explore new ideas.”

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