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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
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Multiplex flows in citation networks.

Benjamin Renoust1,2, Vivek Claver1,2,3, Jean-François Baffier1,4,2

  • 11National Institute of Informatics, Tokyo, Japan.

Applied Network Science
|November 17, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to measure knowledge flow and innovation using directed acyclic graphs (DAGs) and multiplex networks. It offers novel metrics to analyze how scientific influence propagates through citation networks.

Keywords:
Citation networkDirected acyclic graph (DAG)Flow of knowledgeLarge networkMetricsMultiplex network

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Area of Science:

  • Bibliometrics
  • Network Science
  • Information Science

Background:

  • Innovation emerges from collective intelligence and the blending of accumulated knowledge.
  • Citation networks exemplify knowledge transmission but quantifying scientific influence remains challenging.
  • Existing influence measures may not fully capture the complex dynamics of knowledge circulation.

Purpose of the Study:

  • To propose a novel framework for analyzing knowledge circulation and transmission inspired by "streams of knowledge."
  • To investigate the flow of influence within citation networks using directed acyclic graphs (DAGs).
  • To develop and evaluate new measures for multiplex flows in DAGs to better understand citation influence.

Main Methods:

  • Utilizing directed acyclic graphs (DAGs) to model citation networks as ascending flows of influence.
  • Applying the concept of multiplex networks where each citation represents an interaction layer.
  • Designing three novel measures: aggregated flow, sum flow, and selective flow for multiplex DAGs.

Main Results:

  • The study visualizes knowledge diffusion through multiplex networks, offering new perspectives on influence.
  • Analysis of the arXiv HEP-Th dataset reveals insights into the propagation of scientific impact.
  • The proposed framework provides a more granular understanding of how citations influence each other.

Conclusions:

  • The "stream of knowledge" concept, when modeled via multiplex DAG flows, offers a refined approach to understanding scientific innovation.
  • The developed multiplex flow measures provide valuable tools for bibliometric analysis and impact assessment.
  • This research enhances our ability to quantify and visualize the complex dynamics of knowledge transmission in academic fields.