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Link Prediction Between Structured Geopolitical Events: Models and Experiments.

Mayank Kejriwal1

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Event link prediction models geopolitical events. Text-based models like bag-of-words are surprisingly effective, establishing a baseline for future research in computational sciences.

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Area of Science:

  • Computational sciences
  • Complex systems
  • Natural Language Processing (NLP)

Background:

  • Events are increasingly studied in computational sciences.
  • Event link prediction aims to retrieve relevant events for a given input event.
  • Geopolitical events present complex semantic challenges for modeling.

Purpose of the Study:

  • Formalize the event link prediction problem for geopolitical events.
  • Explore the application of representation learning algorithms.
  • Empirically evaluate different modeling approaches using the Global Terrorism Database (GTD).

Main Methods:

  • Formalization of the event link prediction problem.
  • Application of established representation learning algorithms.
  • Empirical study on the Global Terrorism Database (GTD) using information retrieval metrics.

Main Results:

  • Both network-theoretic and text-centric models show considerable signal.
  • Classic text-only models, such as bag-of-words, are difficult to outperform.
  • Established a baseline for event link prediction on the GTD.

Conclusions:

  • Text-only models remain highly competitive for event link prediction.
  • Significant challenges remain in modeling complex geopolitical event semantics.
  • The study provides a foundation and highlights future research directions.