Journal of the Brazilian Computer Society
Identifying communication patterns in social networks is a highly complex challenge. These networks, which are inherently complex in nature, are characterized by sparsely connected graphs.
This study proposes an analysis model that combines the capacity for topological representation of graph-embedding techniques, such as Deep Walk, with the power of data structure identification of neural networks based on self-organizing maps.
The article describes the results of testing the proposed analysis model with Twitter/X retweet data on topics related to vaccination in Brazil.
How to cite: Ciodaro, T., Carmo, V.D., Ferreira, F., Grael, F., Salles, D., & Santini, M. (2024). Visual Social Network Analysis Based on Deep-Walk Graph-Embeddings and Self-Organizing Maps. Anais do XIII Brazilian Workshop on Social Network Analysis and Mining (BraSNAM 2024).
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