I have something of an issue with neural net type visualizations that use many thousands of nodes, which is that they can still only really represent a spatial overview, via grouping, proximity etc of clusters and the density between and within those clusters.
Yes, you can examine node information on click/hover but can you ever find the same node again - not very easily. In which case, what is the real importance or usefulness of the fine detail, and how important is it to adding value to the overview. As Julie Steel mentions in her book Beautiful Visualisation, if not carefully constructed, the many data points can glob together into a mess that is not helpful in order to help the user understand a useful meaning.
I would be more inclined to rethink what the user really needs from the information, how they are going to interact with it, how they are going to act upon or use the information they find. Understand what the important attributes are and consider producing a more tangible prioritized contextual structure for your users to view, interact, interpret and take away.
For example, rather than forcing the user to trawl and interpret the network, it could be more advantageous to automatically identify the order or priority of certain nodes or clusters of nodes with common attributes, so that the takeaways are already calculated. If multiple attributes are of interest, provide a mechanism of changing the pivot points. By the way - I'm not suggesting that tables, or datagrids, or lists or spreadsheets are the answer - they're not.
Consider what problem is it that the visualisation is solving.