mlsanityData to 14 Oct 2026

Interactive Visualization of Protein RINs using NetworKit in the Cloud

arXiv 2203.01263 ↗First posted 2 Mar 2022cs.SI cs.HC cs.LGcited by 1

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Abstract (arXiv)

Network analysis has been applied in diverse application domains. In this paper, we consider an example from protein dynamics, specifically residue interaction networks (RINs). In this context, we use NetworKit -- an established package for network analysis -- to build a cloud-based environment that enables domain scientists to run their visualization and analysis workflows on large compute servers, without requiring extensive programming and/or system administration knowledge. To demonstrate the versatility of this approach, we use it to build a custom Jupyter-based widget for RIN visualization. In contrast to existing RIN visualization approaches, our widget can easily be customized through simple modifications of Python code, while both supporting a good feature set and providing near real-time speed. It is also easily integrated into analysis pipelines (e.g., that use Python to feed RIN data into downstream machine learning tasks).

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 5 of its 63 references that are in the map. First posted on arXiv 2 Mar 2022.

Builds on

5 of 63 references.

References · 5

  1. 2016node2vec: Scalable Feature Learning for Networkscited by 1,918
  2. 2018From Louvain to Leiden: guaranteeing well-connected communitiescited by 385
  3. 2019Machine learning for molecular simulationcited by 110
  4. 2016SNAP: A General Purpose Network Analysis and Graph Mining Librarycited by 56
  5. 2017Maxent-Stress Optimization of 3D Biomolecular Modelscited by 1

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Citing papers per year: 2023: 1.

1 paper.