Choosing Collaboration Partners. How Scientific Success in Physics Depends on Network Positions
August 10, 2016 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Raphael H. Heiberger, Oliver J. Wieczorek
arXiv ID
1608.03251
Category
physics.soc-ph
Cross-listed
cs.DL,
cs.SI
Citations
8
Venue
arXiv.org
Last Checked
3 months ago
Abstract
Physics is one of the most successful endeavors in science. Being a prototypic big science it also reflects the growing tendency for scientific collaborations. Utilizing 250,000 papers from ArXiv.org a prepublishing platform prevalent in Physics we construct large coauthorship networks to investigate how individual network positions influence scientific success. In this context, success is seen as getting a paper published in high impact journals of physical subdisciplines as compared to not getting it published at all or in rather peripheral journals only. To control the nested levels of authors and papers, and to consider the time elapsing between working paper and prominent journal publication we employ multilevel eventhistory models with various network measures as covariates. Our results show that the maintenance of even a moderate number of persistent ties is crucial for scientific success. Also, even with low volumes of social capital Physicists who occupy brokerage positions enhance their chances of articles in high impact journals significantly. Surprisingly, inter(sub)disciplinary collaborations decrease the probability of getting a paper published in specialized journals for almost all positions.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β physics.soc-ph
π
π
The Cartographer
R.I.P.
π»
Ghosted
Networks beyond pairwise interactions: structure and dynamics
R.I.P.
π»
Ghosted
Statistical physics of human cooperation
R.I.P.
π»
Ghosted
Vital nodes identification in complex networks
R.I.P.
π»
Ghosted
Influence maximization in complex networks through optimal percolation
R.I.P.
π»
Ghosted
Scale-free networks are rare
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted