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Related Experiment Video

Updated: Apr 19, 2026

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

Published on: April 18, 2025

Predicting scientific breakthroughs through capacity-based dynamics in citation networks.

Jiazhen Liu¹, Kunal Tamang¹, Dashun Wang^2,3

¹University of Miami, Department of Physics, Coral Gables, Florida 33146, USA.

Physical Review. E

|April 18, 2026

Summary

This summary is machine-generated.

Scientific impact has two phases: established works reinforcing each other and new papers using overlooked ideas. A new

Related Experiment Videos

Last Updated: Apr 19, 2026

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

Published on: April 18, 2025

Area of Science:

Bibliometrics
Network Science
Scientific Discovery

Background:

Scientific impact arises from citation networks exhibiting complex dynamics.
Citation patterns show a dual structure: long-term reinforcement and short-term novelty seeking.
Understanding the drivers of scientific breakthroughs remains a key challenge.

Purpose of the Study:

To reconcile the paradox of citation network dynamics.
To introduce and validate a new metric for predicting scientific impact.
To develop a theoretical framework for knowledge diffusion and breakthrough emergence.

Main Methods:

Analysis of over 30 million scientific papers and 1 billion references across disciplines.
Introduction of a 'capacity' variable to quantify the originality budget of prior knowledge.
Formulation of a stochastic dynamical model coupling novelty erosion with capacity-driven attachment.

Main Results:

Identified a universal double-exponential relationship between capacity and long-term impact.
The model accurately reproduces observed dual-phase citation correlations.
Capacity effectively predicts scientific breakthroughs and distinguishes Nobel Prize-winning papers.

Conclusions:

Capacity serves as a robust early indicator of scientific breakthroughs.
The study provides a unified framework for understanding correlated impact dynamics.
Advances our comprehension of knowledge diffusion and breakthrough emergence in evolving networks.