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Daniel B. Larremore
University of Colorado Boulder
28Publications
14H-index
561Citations
Publications 28
Newest
Faculty at prestigious institutions produce more scientific papers, receive more citations and scholarly awards, and are typically trained at more-prestigious institutions than faculty with less prestigious appointments. This imbalance is often attributed to a meritocratic system that sorts individuals into more-prestigious positions according to their reputation, past achievements, and potential for future scholarly impact. Here, we investigate the determinants of scholarly productivity and mea...
#1Daniel B. Larremore (CU: University of Colorado Boulder)H-Index: 14
Measuring the overlap between two populations is, in principle, straightforward. Upon fully sampling both populations, the number of shared objects—species, taxonomical units, or gene variants, depending on the context—can be directly counted. In practice, however, only a fraction of each population’s objects are likely to be sampled due to stochastic data collection or sequencing techniques. Although methods exists for quantifying population overlap under subsampled conditions, their bias is we...
#1Vidit Agrawal (UA: University of Arkansas)H-Index: 1
#2Andrew B. Cowley (CU: University of Colorado Boulder)H-Index: 1
Last.Woodrow L. Shew (UA: University of Arkansas)H-Index: 18
view all 6 authors...
It is widely appreciated that balanced excitation and inhibition are necessary for proper function in neural networks. However, in principle, balance could be achieved by many possible configurations of excitatory and inhibitory synaptic strengths and relative numbers of excitatory and inhibitory neurons. For instance, a given level of excitation could be balanced by either numerous inhibitory neurons with weak synapses or a few inhibitory neurons with strong synapses. Among the continuum of dif...
#1Caterina De Bacco (Columbia University)H-Index: 4
#2Daniel B. Larremore (CU: University of Colorado Boulder)H-Index: 14
Last.Cristopher Moore (SFI: Santa Fe Institute)H-Index: 42
view all 3 authors...
We present a physically inspired model and an efficient algorithm to infer hierarchical rankings of nodes in directed networks. It assigns real-valued ranks to nodes rather than simply ordinal ranks, and it formalizes the assumption that interactions are more likely to occur between individuals with similar ranks. It provides a natural statistical significance test for the inferred hierarchy, and it can be used to perform inference tasks such as predicting the existence or direction of edges. Th...
#1Daniel B. Larremore (CU: University of Colorado Boulder)H-Index: 14
Measuring the overlap between the var gene repertoires of two Plasmodium falciparum parasites is, in principle, easy. Each parasite genome contains a repertoire of approximately 60 var genes, so upon fully sequencing both parasites' genomes, the number of shared var sequences can be directly counted. In practice, however, only a fraction of each parasite's var repertoire is likely to be sampled due to the difficulties of whole-genome sequencing for var genes and the stochastic sample provided by...
#1Bailey K. Fosdick (CSU: Colorado State University)H-Index: 10
#2Daniel B. Larremore (SFI: Santa Fe Institute)H-Index: 14
Last.Johan Ugander (Stanford University)H-Index: 16
view all 4 authors...
Random graph null models have found widespread application in diverse research communities analyzing network datasets, including social, information, and economic networks, as well as food webs, protein-protein interactions, and neuronal networks. The most popular random graph null models, called configuration models, are defined as uniform distributions over a space of graphs with a fixed degree sequence. Commonly, properties of an empirical network are compared to properties of an ensemble of ...
#1Samuel F. Way (CU: University of Colorado Boulder)H-Index: 6
#2Allison C. Morgan (CU: University of Colorado Boulder)H-Index: 2
Last.Daniel B. Larremore (CU: University of Colorado Boulder)H-Index: 14
view all 4 authors...
A scientist may publish tens or hundreds of papers over a career, but these contributions are not evenly spaced in time. Sixty years of studies on career productivity patterns in a variety of fields suggest an intuitive and universal pattern: Productivity tends to rise rapidly to an early peak and then gradually declines. Here, we test the universality of this conventional narrative by analyzing the structures of individual faculty productivity time series, constructed from over 200,000 publicat...
#1Andrew BerdahlH-Index: 10
#2Uttam BhatH-Index: 4
Last.Christopher P. KempesH-Index: 4
view all 10 authors...
World record setting has long attracted public interest and scientific investigation. Extremal records summarize the limits of the space explored by a process, and the historical progression of a record sheds light on the underlying dynamics of the process. Existing analyses of prediction, statistical properties, and ultimate limits of record progressions have focused on particular domains. However, a broad perspective on how record progressions vary across different spheres of activity needs fu...
#1Leto Peel (UCL: Université catholique de Louvain)H-Index: 8
#2Daniel B. Larremore (SFI: Santa Fe Institute)H-Index: 14
Last.Aaron Clauset (CU: University of Colorado Boulder)H-Index: 30
view all 3 authors...
Across many scientific domains, there is a common need to automatically extract a simplified view or coarse-graining of how a complex system’s components interact. This general task is called community detection in networks and is analogous to searching for clusters in independent vector data. It is common to evaluate the performance of community detection algorithms by their ability to find so-called ground truth communities. This works well in synthetic networks with planted communities becaus...
#1Caterina De BaccoH-Index: 4
#2Eleanor A. PowerH-Index: 1
Last.Cristopher MooreH-Index: 42
view all 4 authors...
Complex systems are often characterized by distinct types of interactions between the same entities. These can be described as a multilayer network where each layer represents one type of interaction. These layers may be interdependent in complicated ways, revealing different kinds of structure in the network. In this work we present a generative model, and an efficient expectation-maximization algorithm, which allows us to perform inference tasks such as community detection and link prediction ...
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