Evolution of the social network of scientific collaborations

@article{Barabasi2002EvolutionOT,
  title={Evolution of the social network of scientific collaborations},
  author={A L Barabasi and H. Jeong and Zolt{\'a}n N{\'e}da and Ersz{\'e}bet Ravasz and Andreas Schubert and Tam{\'a}s Vicsek},
  journal={Physica A-statistical Mechanics and Its Applications},
  year={2002},
  volume={311},
  pages={590-614}
}
Collaboration over time: characterizing and modeling network evolution
TLDR
A novel stochastic model, Stochastic Poisson model with Optimization Tree (Spot) is proposed to efficiently predict any increment of collaboration based on the local neighborhood structure and empirical results show that Spot outperforms Support Vector Regression by better fitting collaboration records and predicting the rate of collaboration.
Characterizing the evolution of collaboration network
TLDR
A method to trace scientific individual's and community's growth process based on community's evolution path combination with quantifiable measurements is proposed and finds out that the lifespan of community is also related to the ability of maintaining its core members meaning that community may last for a longer lifespan if its coreMembers are much more stable.
AddDel Model for the Evolution of Social Networks
  • Shikha Sarathe
  • Computer Science
    Computer Engineering and Intelligent Systems
  • 2020
TLDR
A model in which the edges appear as well disappear based on some parameters is created and whether this model predicts real social networks is discussed.
Drastic events make evolving networks
TLDR
By considering the 1995–2005 time interval and scanning the author-article network evolution with a mobile time window, this work focuses on the properties of the links, as well as on the time evolution of the nodes, and on the kind of the resulting collaboration.
The Impact of the Subgroup Structure on the Evolution of Networks: An Economic Model of Network Evolution
TLDR
The evolutionary model may be applicable not only to describe the structural evolution of networks but also to make network design recommendations in a variety of areas such as WWW-hyperlink networks, business collaboration networks, Peer-To-Peer Networks, and Web2.0 service networks.
The Complex Network of Evolutionary Computation Authors: an Initial Study
TLDR
The network of authors of evolutionary computation papers found in a major bibliographic database is explored, its macroscopic properties are examined, and it is found that the EC co-authorship network yields results in the same ballpark as other networks, but exhibits some distinctive patterns in terms of internal cohesion.
Structure and dynamics of evolving complex networks
TLDR
This thesis introduces various novel processes which dictate the development of a network on a small scale, and uses techniques learned from statistical physics to derive the dynamical and structural properties of the network on the macroscopic scale.
The Complex Network of Evolutionary Computation Authors: an Initial Study
TLDR
The network of authors of evolutionary computation papers found in a major bibliographic database is explored, its macroscopic properties are examined, and it is found that the EC co-authorship network yields results in the same ballpark as other networks, but exhibits some distinctive patterns in terms of internal cohesion.
...
...

References

SHOWING 1-10 OF 69 REFERENCES
Topology of evolving networks: local events and universality
TLDR
A continuum theory is proposed that predicts the connectivity distribution of the network describing the professional links between movie actors as well as the scaling function and the exponents, in good agreement with numerical results.
Organization of growing random networks.
  • P. Krapivsky, S. Redner
  • Computer Science
    Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2001
TLDR
The organizational development of growing random networks is investigated, and the combined age and degree distribution of nodes shows that old nodes typically have a large degree.
Collective dynamics of ‘small-world’ networks
TLDR
Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
Structure of growing networks with preferential linking.
TLDR
The model of growing networks with the preferential attachment of new links is generalized to include initial attractiveness of sites and it is shown that the relation beta(gamma-1) = 1 between the exponents is universal.
Emergence of scaling in random networks
TLDR
A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
Scientific collaboration networks. I. Network construction and fundamental results.
  • M. Newman
  • Computer Science
    Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2001
TLDR
Using computer databases of scientific papers in physics, biomedical research, and computer science, a network of collaboration between scientists in each of these disciplines is constructed, and a number of measures of centrality and connectedness in the same networks are studied.
Evolution of networks with aging of sites
  • Dorogovtsev, Mendes
  • Mathematics
    Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics
  • 2000
TLDR
It is found both from simulation and analytically that the network shows scaling behavior only in the region alpha<1, when alpha increases from -infinity to 0, and the exponent gamma of the distribution of connectivities grows from 2 to the value for the network without aging.
Social Network Analysis
This paper reports on the development of social network analysis, tracing its origins in classical sociology and its more recent formulation in social scientific and mathematical work. It is argued
Epidemic spreading in scale-free networks.
TLDR
A dynamical model for the spreading of infections on scale-free networks is defined, finding the absence of an epidemic threshold and its associated critical behavior and this new epidemiological framework rationalizes data of computer viruses and could help in the understanding of other spreading phenomena on communication and social networks.
...
...