Taking a broad view of the area, it's interesting that tables and networks are both fundamental ways of representing data in many situation, and often complementary, but the technology for working with tables is far more mature. This spans the low end of representation (CSV files, data frames in pandas) to database systems that provide essential capabilities like query languages and consistency checking, to entire application platforms like Oracle. The equivalent systems for networks aren't as mature. There's still a big opportunity to meet this need, and you can see a lot of progress in technology like NetworkX and Neo4J.

A great service that network programming systems provide, as highlighted in some of the comments here, is simply to provide a framework for integrating useful functions for operating or computing on networks so they can be delivered to a broad audience. To illustrate, in the land of tables, if I need to join two tables, and count rows or sort and show the top-N, I know what software on my computer does that. If I have a network and want to find a shortest path or do some other simple pattern matching on paths, and maybe show the result graphically, what am I supposed to use? It's really not clear.

And, in 2023, why not both? Applications need both tabular and network views of data, and in some ways they are not that different. That the popular relational data platforms have not already rushed in to fill this need give some reason to pause and consider the situation further or maybe is an argument that tables and networks should have similar importance.

From real life, maybe one issue is that the technology for computing on tables is pretty well understood and manageable, but there are a lot of sharp edges around combinatorial structures like networks. From a recent real-life example, say I just want to make an attributed network representing employees in a company, and match them for an internal project. Is this as easy as sorting rows in a table? Hardly. Can you even find a textbook pseudocode algorithm and code it in a graph query language that is recognizable as that algorithm? These look like tough problems. At least NetworkX and systems like it provide a framework to integrate those implementation for a developer audience.