Community Structure and the Evolution of Interdisciplinarity in Slovenia's Scientific Collaboration Network

@article{Luar2014CommunitySA,
  title={Community Structure and the Evolution of Interdisciplinarity in Slovenia's Scientific Collaboration Network},
  author={Borut Lu{\vz}ar and Zoran Levnajic and Janez Povh and Matja{\vz} Perc},
  journal={PLoS ONE},
  year={2014},
  volume={9}
}
Interaction among the scientific disciplines is of vital importance in modern science. Focusing on the case of Slovenia, we study the dynamics of interdisciplinary sciences from to . Our approach relies on quantifying the interdisciplinarity of research communities detected in the coauthorship network of Slovenian scientists over time. Examining the evolution of the community structure, we find that the frequency of interdisciplinary research is only proportional with the overall growth of the… 

Figures and Tables from this paper

Core-periphery dynamics in collaboration networks: the case study of Slovenia

Analysis of core-periphery structure and transition dynamics of individuals between the core and periphery in collaboration networks of Slovenian researchers over 44 years is presented.

Analysis of Slovenian research community through bibliographic networks

This paper addresses performance of Slovenian research community using bibliographic networks between the years 1970 and 2015 from various aspects which determine prolific science including productivity, collaboration, internationality, and interdisciplinarity.

Fragmented Romanian Sociology: Growth and Structure of the Collaboration Network

The structure of the collaboration network formed by the academics working full-time within all the 17 sociology departments across Romania is explored, and it is shown that the network is sparse and fragmented, and that it constitutes an environment that does not promote the circulation of new ideas and innovation within the field.

Evolution and structure of scientific co-publishing network in Korea between 1948–2011

The results show that both the numbers of publications and authors in Korea have grown exponentially in a 64 year time frame and implies a potential vulnerability for the network and its wider context.

The emergent integrated network structure of scientific research

It is suggested that complex and dynamic patterns of knowledge emerge from scientific research, and that structures reflecting intellectual integration may be beneficial for obtaining scientific insight.

Mapping the interdisciplinarity in information behavior research: a quantitative study using diversity measure and co-occurrence analysis

Although variety of disciplines in this field augmented significantly, the distribution of disciplines is unbalanced and concentrated on some dominant disciplines such as computer science, engineering, psychology, social science and medicine, etc.

Analyzing link dynamics in scientific collaboration networks ℄ A social yield based perspective

Observation indicate that certain observed behavior like presence of large number of small sized communities and highly dynamic behavior of the links in collaboration networks can be explained based on the distribution of social yield of these collaborations.

Collaboration patterns in the German political science co-authorship network

The findings indicate that German political science is structured by multiple overlapping research clusters with a dominance of the subfields of international relations, comparative politics and political sociology.

A new network model for the study of scientific collaborations: Romanian computer science and mathematics co-authorship networks

It is found that the proposed networks are smaller and denser than the co-authorship networks, have a better defined community structure, and directly represent the results of collaborative endeavors by focusing on the actual outcome, i.e., published papers.

Link dynamics in scientific collaboration networks

Observations indicate that certain properties of scientific collaboration networks like community dynamics and resilience can be explained by the distribution of social yield among the collaborations.
...

References

SHOWING 1-10 OF 28 REFERENCES

Growth and structure of Slovenia's scientific collaboration network

  • M. Perc
  • Computer Science
    J. Informetrics
  • 2010

Detecting the overlapping and hierarchical community structure in complex networks

The first algorithm that finds both overlapping communities and the hierarchical structure is presented, based on the local optimization of a fitness function, enabling different hierarchical levels of organization to be investigated.

Uncovering the overlapping community structure of complex networks in nature and society

After defining a set of new characteristic quantities for the statistics of communities, this work applies an efficient technique for exploring overlapping communities on a large scale and finds that overlaps are significant, and the distributions introduced reveal universal features of networks.

Community detection in graphs

Quantifying social group evolution

The focus is on networks capturing the collaboration between scientists and the calls between mobile phone users, and it is found that large groups persist for longer if they are capable of dynamically altering their membership, suggesting that an ability to change the group composition results in better adaptability.

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

Evolution of Controllability in Interbank Networks

The notion of network controllability is extended to detect the financial institutions, i.e. the drivers, that are most crucial to the functioning of an interbank market, implying that policies have to be adjusted to the time scales in order to be effective.

Finding Statistically Significant Communities in Networks

OSLOM (Order Statistics Local Optimization Method), the first method capable to detect clusters in networks accounting for edge directions, edge weights, overlapping communities, hierarchies and community dynamics, is presented.

Self-Organization and Identification of Web Communities

This work shows that the Web self-organizes and its link structure allows efficient identification of communities and is significant because no central authority or process governs the formation and structure of hyperlinks.

Resolution limit in community detection

It is found that modularity optimization may fail to identify modules smaller than a scale which depends on the total size of the network and on the degree of interconnectedness of the modules, even in cases where modules are unambiguously defined.