Citation interactions among computer science fields: a quantitative route to the rise and fall of scientific research

  • Tanmoy Chakraborty
  • Sandipan Sikdar
  • Niloy Ganguly
  • Animesh Mukherjee
Original Article


In this work, we propose for the first time a suite of metrics that can be used to perform post-hoc analysis of the temporal communities of a large-scale citation network of the computer science domain. Each community refers to a particular research field in this network, and therefore, they act as natural sub-groupings of this network (i.e., ground-truths). The interactions between these ground-truth communities through citations over the real time naturally unfold the evolutionary landscape of the dynamic research trends in computer science. These interactions are quantified in terms of a metric called inwardness that captures the effect of local citations to express the degree of authoritativeness of a community (research field) at a particular time instance. In particular, we quantify the impact of a field, the influence imparted by one field on the other, the distribution of the “star” papers and authors, the degree of collaboration and seminal publications to characterize such research trends. In addition, we tear the data into three subparts representing the continents of North America, Europe and the rest of the world, and analyze how each of them influences one another as well as the global dynamics. We point to how the results of our analysis correlate with the project funding decisions made by agencies like NSF. We believe that this measurement study with a large real-world data is an important initial step towards understanding the dynamics of cluster-interactions in a temporal environment. Note that this paper, for the first time, systematically outlines a new avenue of research that one can practice post community detection.


Community Detection Citation Network Research Trend Funding Decision Influential Paper 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Wien 2014

Authors and Affiliations

  • Tanmoy Chakraborty
    • 1
  • Sandipan Sikdar
    • 1
  • Niloy Ganguly
    • 1
  • Animesh Mukherjee
    • 1
  1. 1.Department of Computer Science and EngineeringIndian Institute of TechnologyKharagpurIndia

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