NEExT

v0.2.13 suspicious
4.0
Medium Risk

Network Embedding Experimentation Toolkit - A powerful framework for graph analysis, embedding computation, and machine learning on graph-structured data

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits signs of potential risk due to the new or inactive maintainer's account and lack of proper author identification, despite having low risks in other categories such as network, shell, and obfuscation.

  • New or inactive maintainer's account
  • Lack of proper author name
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires network functionality.
  • Shell: No shell execution patterns detected, indicating low risk of command injection or similar attacks.
  • Obfuscation: The observed patterns appear to be related to model evaluation and embedding collection, which are common practices in machine learning frameworks and do not suggest malicious obfuscation.
  • Credentials: No suspicious patterns indicating credential harvesting were detected.
  • Metadata: The maintainer has a new or inactive account and lacks a proper author name, raising some suspicion but not definitive evidence of malice.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

  • graphs: model.eval() decoder.eval() val_losses
  • val() decoder.eval() val_losses = []
  • embeddings self.model.eval() all_embeddings = [] with torch.no
✓ 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

Repository ashdehghan/NEExT 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 NEExT 
Develop a social network analysis tool using the NEExT package in Python. This tool will allow users to upload a social network dataset (in the form of a CSV file containing user IDs and connections), perform various types of graph analysis, compute node embeddings, and visualize the results. The application should include the following features:

1. **Graph Import**: Allow users to import their social network data as an edge list or adjacency matrix.
2. **Graph Visualization**: Implement a feature to visualize the imported graph using NEExT's visualization capabilities.
3. **Node Embedding Computation**: Use NEExT to compute node embeddings which capture the structural role of each node within the graph. Provide options for different embedding algorithms supported by NEExT.
4. **Community Detection**: Utilize NEExT’s community detection algorithms to identify clusters of nodes within the graph.
5. **Network Metrics Calculation**: Calculate and display key network metrics such as degree centrality, betweenness centrality, and clustering coefficient for each node.
6. **Visualization of Embeddings**: Provide an interactive plot where users can explore the computed node embeddings, possibly using dimensionality reduction techniques like t-SNE or PCA.
7. **Export Results**: Allow users to export the computed embeddings and network metrics to a CSV file for further analysis.
8. **User Interface**: Develop a simple web-based UI using Flask or Streamlit where users can interact with the application and view results.

The goal is to create a comprehensive, user-friendly tool that leverages the power of NEExT for social network analysis, making it accessible for researchers and analysts without deep expertise in graph theory or network science.