Topic discovery and diversity

General description

• docs:
  • Pham-CNA2021.pdf describes the model and results
  • CNA21-242_Pham.pdf is the presentation file for CNA21.
  • future-considerations.md describes some future considerations and directions for the models and analyses
• feedbacks contains all the comments and feedbacks received from multiple people (listed in the report's acknowledgement section)
• src:
  • topic_discovery.R: definitions for TopicDiscovery class (initialization and simulation)
  • topic_analysis.R: functions for running diversity analysis and time analysis
• scripts:
  • rmd2rscript.R: convert Rmd files in this folder to R scripts to run non-interactively
  • sim-block.R[md]: simulation with block/group models (i.e. using sample_sbm)
  • sim-nonblock.R[md]: simulation with nonblock models (i.e. using sample_[pa,gnp,smallworld])
  • analyze-diversity-through-time.R[md]: topic diversity time analysis scripts
  • vis-demo.Rmd: generate some demo plots about model parameters
  • vis-analysis.Rmd: analysis plots for all simulations
• svproc/cnasubm: latex files to generate CNA21 submission, along with necessary figure and bib files. The final document is also in docs/Pham-CNA2021.pdf.
• data: the data and analyses generated from scripts can be downloaded at https://doi.org/10.6084/m9.figshare.17086832.v1

Notes

• To run simulation/visualization scripts in the scripts folder (scripts/sim-* files), move to main folder first, this is because they were written to be run from main folder, so the data file paths in the params_df are saved as data/XXX/data-001234.rds instead of ../data/XXX/data-001234.rds. Then they were moved to the scripts folder for organization purposes. This does need improvement in the future.

• For update functions:

  • update_bipartite_topicagent and update_learnt_topic are quite manual and slow but rid off duplicates and already learnt subjects
  • update_via_matmul is much faster but the beta route doesn't choose a friend of aoi before hand

Citation

The results are publised and you can refer to the following:

Pham, T. (2022). Modelling the Effects of Self-learning and Social Influence on the Diversity of Knowledge. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications X. COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol 1016. Springer, Cham. https://doi.org/10.1007/978-3-030-93413-2_4

@InProceedings{10.1007/978-3-030-93413-2_4,
  author="Pham, Tuan",
  editor="Benito, Rosa Maria
  and Cherifi, Chantal
  and Cherifi, Hocine
  and Moro, Esteban
  and Rocha, Luis M.
  and Sales-Pardo, Marta",
  title="Modelling the Effects of Self-learning and Social Influence on the Diversity of Knowledge",
  booktitle="Complex Networks {\&} Their Applications X",
  year="2022",
  publisher="Springer International Publishing",
  address="Cham",
  pages="42--53"
}