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DAC: Descendant-aware clustering algorithm for network-based topic emergence prediction

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  • Jung, Sukhwan
  • Segev, Aviv

Abstract

Topic emergence detection aids in pinpointing prominent topics within a given domain, providing practical insights into all interested parties on where to focus the limited resources. This paper employs the network-based topic evolution approach to overcome limitations in text-based topic evolution, providing prospective topic emergence prediction capabilities by representing emergent topics by their ancestors. A descendant-aware clustering algorithm is proposed to generate non-exhaustive and overlapping clusters, utilizing the pace of collaborations and structural similarities between topics with iterative edge removal and addition processes. Over 100 datasets specific to a research topic were extracted from the Microsoft Academic Graph dataset for the experiments, where the proposed algorithm consistently outperformed existing clustering algorithms in generating clusters with a higher likelihood of being ancestors to an emergent topic up to three years in the future. Regression-based cluster filtering using five structural cluster features and topic cluster qualities showed that the prediction performance can be enhanced by automatically classifying undesirable clusters from previously known data. The results showed that the proposed algorithm can enhance topic emergence predictions on a wide range of research domains regardless of their maturities, popularities, and magnitudes without having access to the data in the predicted year, paving a road to prospective predictions on emergent topics.

Suggested Citation

  • Jung, Sukhwan & Segev, Aviv, 2022. "DAC: Descendant-aware clustering algorithm for network-based topic emergence prediction," Journal of Informetrics, Elsevier, vol. 16(3).
    • Handle: RePEc:eee:infome:v:16:y:2022:i:3:s1751157722000724
    DOI: 10.1016/j.joi.2022.101320
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