Question: Adapting depgraph for custom external data (physics hypergraph visualization)?

[hcsn-theory]

Hey @zackarydev ,

​I noticed you recently cloned my hcsn-theory repository, which is awesome—it is always great to connect with other builders working with hypergraphs.

​I was taking a look at depgraph and the clustering UI you have built is absolutely stunning. I am currently building a Python-based physics simulation engine that generates dynamic discrete causal networks (hypergraphs), and your visualizer is exactly the kind of interface I have been trying to achieve to show how "topological defects" group together into clusters.

​I have a quick architectural question: How difficult would it be to feed external JSON or CSV data into depgraph? Currently, my simulation spits out a raw edge list and node states. I would love to write an exporter to format my engine's output so it can be rendered by your UI. Any pointers on the best way to inject custom data into your pipeline would be hugely appreciated!

​Thanks for building this, the UI is incredible

[zackarydev]

@hcsn-theory amazing work on your theory. Simulating our universe will require a fundamentally different approach than what is currently available. DFT, Lattice gauge theory, quantum computing etc. are all ... unscalable? I dream of the day when we can simulate a drop of water, or a real cell, or fire. Alas...

I was actually curious about how this would integrate with HCSN. Could be my project for next weekend if you want to focus on other things than UI..

depgraph is eventually going to be capable of hypergraph navigation. The current implementation focuses on two major hierarchies: Nodes, Clusters. Those nodes and clusters are connected via various Edge layers (edge types). I think there's 4-5 different types of edges. But I'm adding more everyday... there's just so many ways nodes can be related to each other.

depgraph should also theoretically be realtime, it's highly untested, but partial functionality certainly did exist at some point during the development phase. This is a rapidly iterating prototype, features break constantly... But the philosophy of this project is for realtime ingestion of nodes and edges, and for consistent updates to clusters.

For data imports/exports:

I think CSV files could be easier to stream, just append new lines to the file while the simulator runs, and depgraph could instantly rerender.

For node data:

depgraph requires a "importance" score to nodes, dictates node size. Currently AI generated, with AST support. Node positions could also be provided externally, but hypergraphs are polydimensional and doing so could prove difficult.

A few notes:

• I'd like to eliminate the idea of a "cluster", node+edges should convey all the info needed to hierarchically organize that information.
• Completely untested with large nodes/edges lists (performance unknown)
• I'd like to investigate different zoom levels. Currently the two major hierarchies servers as the proof of concept for hierarchical information clustering. Source code, data is certainly polydimensional, HCSN too. Having different zoom-levels would be solved by eliminating the clusters. But I fear that a complete rewrite of this prototype will be necessary to ensure this type of hypergraph navigability. D3 is simply not powerful enough?...
• In future iterations, I'd like for runtime analysis of programs. A bit with how your rewrite rules are executed. AST provides the name of the function that wrote to a variable. And it also provides the name of the function that called another function. There's huge similarities with the rewrite rules of HCSN I would assume.

Let me know if you have any features you'd like for HCSN. I was planning on looking into your rewrite rules more concretely soon.

[hcsn-theory]

@zackarydev 100% agreed on the simulation limits. Trying to force emergent reality into a pre-defined 3D lattice or standard quantum framework always hits a wall. The geometry has to emerge from the substrate itself. I'd absolutely love to see what depgraph can do with HCSN.

hcsn_sample.csv

​I'm totally down for this to be a weekend project. I actually just put together a CSV exporter based on your notes to make ingestion easy.

​A few notes on how the HCSN data maps to your architecture:

• ​Streaming CSV: I generated a sample file (hcsn_sample.csv) that streams exactly how you mentioned. It just appends NODE and EDGE rows at each time step t.

• ​Node "Importance" Score: I mapped your importance score directly to HCSN's xi value. xi basically measures the localized structural tension (how "active" a node is). So as topological defects (proto-particles) emerge in the simulation, those nodes will naturally have high importance scores and should physically swell up in your UI.

• ​Eliminating Clusters: Completely agree with your philosophy here. Pre-defined clusters shouldn't be necessary. If the hypergraph topology is rendered raw, the particles should naturally clump together via the force-directed layout just based on the edge density.

• ​AST Similarities: Your intuition about the AST is spot on. A rewrite rule in this framework acts exactly like a localized function call mutating a variable state in a program.

​I've attached the hcsn_sample.csv to this thread. It's about 190k rows of streaming data. I force-injected two "defects" around t=80 and t=150 so you have some actual structures to visualize right out of the gate.

​Let me know if the CSV schema needs any tweaking for your ingestion pipeline. Really excited to see how this looks!

[zackarydev]

• ​Streaming CSV: I generated a sample file (hcsn_sample.csv) that streams exactly how you mentioned. It just appends NODE and EDGE rows at each time step t.

I've implemented a rudimentary version of CSV streaming. I'm improving the initial placement of nodes via inertia, and being able to navigate it the same way depgraph can be navigated... Also, there's a jitter that occurs, most likely because of the inertia.

• ​Node "Importance" Score: I mapped your importance score directly to HCSN's xi value. xi basically measures the localized structural tension (how "active" a node is). So as topological defects (proto-particles) emerge in the simulation, those nodes will naturally have high importance scores and should physically swell up in your UI.

The xi values of HCSN seem to be generating very large differences (ex: 956074.1281 vs. 0.0039). Since this is tied to node sizes in the UI, depgraph's visualizer layer probably caps it to a limit, losing visual comparability (is xi normalizable?), or is another visual purpose desired?

...results pending...

[hcsn-theory]

Great progress for just a few days, @zackarydev ,

Regarding the jittering: Since the CSV is constantly streaming new nodes and edges, the force-directed layout is struggling to find equilibrium. If depgraph has a damping or friction setting, bumping that up slightly should smooth out the bouncing.

Regarding the huge xi values: That's actually expected! A few dense clusters naturally hoard the rewrite activity, causing their values to explode while the background stays near zero. Because it scales exponentially, mapping the node size linearly will break your UI. Try using a log scale like Math.log10(xi + 1), it's looks beautiful . This keeps background nodes small while the active ones scale up perfectly without filling the screen.

If it's easier for your frontend, I can pre-normalize the xi column directly in Python so the CSV just feeds you flat floats between 0.0 and 1.0.

Let me know what you prefer!