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networkit
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Software Engineering or AI or Data Science?
In your case, I would really avoid AI ML DS altogether unless you believe you have the theoretical prerequisites. The coding part in AI ML DS is not like your typical software. It is scientific code and you must understand what's going on in your program with respect to trainable parameters. Here is an example.
networkit
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NetworkX – Network Analysis in Python
If you need more speed (but less features), check out networkit - https://networkit.github.io/
- NetworKit: Herramientas de código abierto en expansión para el análisis de redes a gran escala 🚀
What are some alternatives?
euler - A distributed graph deep learning framework.
NetworkX - Network Analysis in Python
Generalizing-Lottery-Tickets - This repository contains code to replicate the experiments given in NeurIPS 2019 paper "One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers"
nload - Real-time network traffic monitor
molecule-generation - Implementation of MoLeR: a generative model of molecular graphs which supports scaffold-constrained generation
incubator-graphar - An open source, standard data file format for graph data storage and retrieval.
ProGraML - A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations
python-igraph - Python interface for igraph
Graphia - A visualisation tool for the creation and analysis of graphs