Supervised Machine Learning sandbox used to explore different message passing and influence algorithms from various synthetic datasets. Used as a means to research Addressing Health Disparities through Improved Health Literacy in Minority Populations using AI/ML Models and Social Network Analysis. Created to analyze and potentially improve how minority populations access and comprehend health infromation by leveraging predictive and pattern detection using Machine Learning.
- Interactive Visualization: Leverages NetworkX and Matplotlib to visualize graph structures, node influences, and model predictions.
- SHAP Explainer: Utilized to measure of feature importance by computing Shapley values
- Health Equity Focus: Tailored to study and address health disparities in underserved or misrepresented communities.
- Misinformation Modeling: Analyzes and models the spread of health-related information (or misinformation) across social networks, providing actionable insights for intervention strategies.
- Sample result of 100 nodes using NetworkX synthetic graph generator
- Metrics extracted from running the model Click here to view metrics
- Training Loss
- SHAP Feature importance summary
- Node activation
To get a local copy up and running, follow these steps:
- Clone the repository:
git clone https://github.com/franciscomartinez45/Social-Network-Analysis.git
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GTgraph: This project utilizes GTgraph, a suite of synthetic random graph generators developed for the 9th DIMACS Shortest Paths Challenge. GTgraph supports various classes of graphs, including:
- Input graph instances used in the DARPA HPCS SSCA#2 graph theory benchmark (version 1.0).
- Erdős-Rényi random graphs.
- Small-world graphs based on the Recursive Matrix (R-MAT) model.
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NetworkX: Utilize synthetic graph generators from NetworkX such as Barabasi-Albert and Zachary's Karate Club