NetworkX isn't a bad library, nor is it only suitable for "babies". Yeesh. Actual people work on this stuff, you know, and they may have different goals, requirements, etc than you. This whole post reeks of someone who's so in love with being a maverick speaking uncomfortable truths that they've lost sight of this human element.
If you're offended by (say) research papers that fail to break enough new ground to satisfy you, sorry about your luck, but again, not everyone's optimizing the same function as you. You'll probably do better bringing people around when you're not implying (or outright saying) their work is shit.
I have to say I agree. NX is totally fine for the applications it was built for. I appreciate the simple API and the flexibility of using pure Python. There's a reason it's so popular.
I'm not an expert in graph neural networks, so can't really say much about the novelty of Node2Vec. But I do think it's often misguided to judge scientific work as trivial or incremental in retrospect. Specially in a relatively young field like deep learning, where four years (Node2Vec is from 2016) is a long time.
I never said NX is bad, but it is for "baby" graphs. NX can't scale past a few hundred thousand nodes.
I appreciate NX's place in the ecosystem, but its implementation leaves a large gap to be filled for an intermediate library that is a Pandas analogue for graphs.
If you're offended by (say) research papers that fail to break enough new ground to satisfy you, sorry about your luck, but again, not everyone's optimizing the same function as you. You'll probably do better bringing people around when you're not implying (or outright saying) their work is shit.