Hypergraph Cognitive Networks

Citation

Please, refer to the following work:

Citraro S., De Deyne S., Stella M., Rossetti G. (2023) Towards hypergraph cognitive networks as feature-rich models of knowledge. [ArXiv to appear]

Use

This repository contains the basic analytical pipeline of the data we used in the work cited above.

Data includes:

• Features from the Glasgow Norms;
• English Free Associations from the Small World of Words project.

An example notebook contains the basic pipeline of the work:

• Data Preprocessing;
• Graph and Hypergraph-based representations of Free Associations;
• Features' Aggregation Strategies based on the above representations;
• Predicting a Target Feature (e.g., ground-truth concreteness) based on the other aggregated features;

Other details:

• Graph-based representations include the following strategies:
  • G123 Ego-Network.
  • Community Detection based representations: Louvain, EVA, Lemon;
• Prediction:
  • Random Forest Regressor;
  • Evaluation with RMSE, R2.