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Which Python library is best for large-scale graph analysis in social networks?

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Growing 75 pts 0 followers
Bearys Institute of Technology · Posted
Structural Engineering IoT & Embedded Systems Materials Science

Context

I want to start using federated learning for my research on privacy-preserving healthcare analytics. The datasets involve patient records from three hospitals and we cannot share data across sites.

What I am looking for:

  • Python frameworks that are actively maintained and used in research
  • Any frameworks with good support for heterogeneous data distributions (non-IID)
  • Something that has a reasonably gentle learning curve for a researcher (not a systems engineer)

I have looked at PySyft and Flower. Any experience with either, or other alternatives I should consider?

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9 Replies

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Om Prakash Growing 55 pts · Accepted answer

For Zotero vs BibTeX management, I moved to Zotero + Better BibTeX 3 years ago and have not looked back.

My workflow:

  1. Zotero browser connector captures papers instantly from ArXiv, Google Scholar, ACM DL, IEEE Xplore
  2. Better BibTeX generates a clean .bib file that auto-updates whenever I add a paper
  3. Overleaf directly pulls from the .bib file via Dropbox sync

One tip: create a Zotero collection per paper/project. When you export the .bib, export only that collection to keep it clean.

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Replying to Om Prakash
Lakshmi Devi Growing 75 pts · Accepted answer

This is a known issue with XLM-R on code-mixed data. The MuRIL preprint has a section specifically comparing these.

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Zara Hussain Growing 55 pts · Accepted answer

FDR (Benjamini-Hochberg) is widely accepted and preferable to Bonferroni when you have many tests and limited power. The key is to be explicit about which correction you used and why.

On replication: No independent cohort is a genuine limitation but not a dealbreaker for rare disease research, where cohort availability is a known constraint. Many high-quality papers acknowledge this and still get published in good journals. Frame it as future work.

Power analysis: Yes, report it. Showing you are aware of the limitation is better than not mentioning it.

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Replying to Zara Hussain
Ekta Choudhary Starting 45 pts · Accepted answer

The Fulbright SoP is very different from a typical PhD SoP. It needs to focus on cultural exchange and diplomacy, not just research.

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Suresh Iyer · Accepted answer

CSIR-UGC NET preparation: past papers are the most important resource. Everything else is secondary.

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Replying to Suresh Iyer
Pooja Nambiar Growing 115 pts · Accepted answer

+1 to everything said above. My experience was identical.

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Indira Balan · Accepted answer

As a senior faculty member (25 years in the system), I want to offer a different perspective. Yes, the pressures are real. But I have also seen the other side: students who produce excellent work, publish honestly, and build reputations over time. The system is imperfect but not entirely broken.

What has actually helped my students: choosing venues carefully (a few strong papers rather than many weak ones), developing genuine collaborations rather than transactional coauthorships.

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Hemant Patwa · Accepted answer

FDR (Benjamini-Hochberg) is widely accepted and preferable to Bonferroni when you have many tests and limited power. The key is to be explicit about which correction you used and why.

On replication: No independent cohort is a genuine limitation but not a dealbreaker for rare disease research, where cohort availability is a known constraint. Many high-quality papers acknowledge this and still get published in good journals. Frame it as future work.

Power analysis: Yes, report it. Showing you are aware of the limitation is better than not mentioning it.

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Amit Joshi Starting 15 pts · Accepted answer

This question comes up a lot. The answer really depends on your specific field and what your committee values.