🤖 AI Analysis

Final verdict: SUSPICIOUS

The package adjeff v0.6.0 has significant metadata risks due to missing author information and an unverified repository link, which raises suspicion but does not definitively indicate malicious intent.

Missing maintainer's author name
Unverified repository link

Per-check LLM notes

Metadata: The maintainer's author name is missing and the repository is not found, raising concerns about the legitimacy of the package.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

psf, device=device) model.eval() return model(scene) # type: ignore[no-any-return] #

✓ 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: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ 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 adjeff

Create a social network analysis tool using the 'adjeff' library. This tool will allow users to input a dataset representing a social network, where nodes represent individuals and edges represent relationships between them. The application should perform several analyses to highlight key features of the network such as centrality measures, clustering coefficients, and community detection.

Steps to develop the application:
1. Start by setting up a user-friendly interface that allows users to upload a CSV file containing the network data. Each row should represent a relationship between two individuals (e.g., source, target).
2. Utilize the 'adjeff' library to create an adjacency matrix from the uploaded data. This matrix will be the foundation for all subsequent analyses.
3. Implement functionality to calculate various centrality measures (degree, betweenness, closeness) using 'adjeff'. Display these metrics in a clear, visual format for each node.
4. Use 'adjeff' to compute clustering coefficients for each node, providing insights into the local density of connections within the network.
5. Integrate community detection algorithms provided by 'adjeff' to identify clusters or communities within the network. Visualize these communities in an interactive graph.
6. Add a feature to simulate the spread of information or influence through the network based on the adjacency effects modeled by 'adjeff'. Allow users to specify parameters like initial seed nodes and transmission probability.
7. Finally, include an option for users to save their analysis results in a downloadable report format (PDF or Excel).

Suggested Features:
- Interactive visualization of the network graph with adjustable layout options.
- Real-time updates to centrality measures and clustering coefficients as nodes/edges are added or removed.
- Comparison tools to assess changes in network properties over time or under different conditions.
- Advanced settings for customizing simulation parameters in the adjacency effect model.

How 'adjeff' is utilized:
- For generating the adjacency matrix from raw data inputs.
- In calculating various network metrics and properties.
- For implementing community detection and network simulation functionalities.