🤖 AI Analysis

Final verdict: SAFE

The Hypergraph Analysis Toolbox (HAT) appears to be a legitimate tool for hypergraph analytics with low risks associated with network and shell operations. There is no indication of malicious activity or supply-chain attack.

Low network risk score of 3 out of 10, suggesting potential data fetching but no exposure of sensitive information.
Shell risk score of 1 out of 10, indicating no shell execution patterns.

Per-check LLM notes

Network: Network calls are likely for fetching datasets and schemas, which is common for analysis tools but should be reviewed to ensure they do not expose sensitive information.
Shell: No shell execution patterns detected, indicating no immediate risk from command execution.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

ents/datasets" response = requests.get(api_url) if response.status_code == 200: fi
asets/" + file response = requests.get(url) if response.status_code == 200: with o
hif_schema.json" schema = requests.get(url).json() validator = fastjsonschema.compile(schema)

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: umich.edu>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Jpickard1/Hypergraph-Analysis-Toolbox appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with HypergraphAnalysisToolbox

Create a mini-application named 'HyperGraphVisualizer' that leverages the 'HypergraphAnalysisToolbox' Python package to analyze and visualize complex datasets as hypergraphs. The goal of this application is to provide researchers and data analysts with an intuitive tool to explore relationships within their data that cannot be easily represented using traditional graph structures. Here are the key steps and features for building this application:

1. **Setup Environment**: Begin by setting up a Python virtual environment and installing necessary packages including 'HypergraphAnalysisToolbox'. Ensure that all dependencies required by the toolbox are also installed.

2. **Data Input**: Design a user-friendly interface that allows users to upload their dataset in various formats such as CSV, Excel, or JSON. The application should support both single files and batch uploads for large datasets.

3. **Hypergraph Construction**: Utilize the 'HypergraphAnalysisToolbox' to convert uploaded data into a hypergraph model. This involves defining nodes based on entities in the dataset and edges (hyperedges) that represent multi-way relationships among these entities. The toolbox's functions for constructing hypergraphs from raw data should be central here.

4. **Analysis Tools**: Implement analysis tools within the application that use 'HypergraphAnalysisToolbox' functionalities. These could include measures like node centrality, hyperedge density, clustering coefficients, and other metrics relevant to understanding the structure and dynamics of the hypergraph. Provide visual indicators and summaries for each metric calculated.

5. **Visualization**: Develop an interactive visualization component that renders the constructed hypergraph. Users should be able to customize the view by adjusting parameters such as node size, edge thickness, color schemes, etc. Use the visualization capabilities provided by the 'HypergraphAnalysisToolbox' or integrate third-party libraries if necessary.

6. **Export Options**: Allow users to export the analyzed results and visualizations in common file formats such as PNG, PDF, or SVG. Additionally, offer options to save the hypergraph model itself in a format that can be imported back into the application or used in other software.

7. **Documentation & Support**: Finally, create comprehensive documentation that guides users through the setup process, data input/output procedures, and available analysis/visualization options. Include examples and case studies demonstrating the application of 'HyperGraphVisualizer' on real-world datasets.

By following these steps and utilizing the 'HypergraphAnalysisToolbox', you will have developed a powerful yet accessible tool for hypergraph analysis and visualization.