pygraphistry VS NetworkX

Extra fun: We find most enterprise/gov graph analytics work only requires 1-2 attributes to go along with the graph index, and those attributes often are already numeric (time, $, ...) or can be dictionary-encoded as discussed here (categorical, ID, ...)... so even 'tough' billion scale graphs are fine on 1 gpu.

Early, but that's been the basic thinking into our new GFQL system: slice into the columns you want, and then do all the in-GPU traversals you want. In our V1, we keep things dataframe-native include the in-GPU data representation, and are already working on the first extensions to support switching to more graph-native indexing for steps as needed.

Ex: https://github.com/graphistry/pygraphistry/blob/master/demos...

If you enjoy this kind of thinking, we recently released GFQL for dataframe-native graph querying & compute

Imagine Neo4j Cypher, except no need for a database -- just import it -- and automatically vectorizes for significantly faster CPU+GPU performance. This is fundamentally similar to the kinds of implementations a datalog approach enables. (And indeed one of the alternative interfaces we were considering!)

We've run it on 100M+ edge graphs on some of the cheapest GPUs you can get, and are getting ready for the next rev with aggregate compute: https://github.com/graphistry/pygraphistry/blob/master/demos...

This is a great article and fun to see fundamental concepts get (re)discovered here!

A perspective that we can generalize from the hierarchy discussion is to think about tool-for-the-job: what is the 'content' job, and what 'jobs' graphs will do? We think about this a lot as we work on problems like how to make it easy to explore 100,000X+ more relationships on screen than they're showing: https://github.com/graphistry/pygraphistry .

First, what do graph visualizations do?

- They let us see the relationships in data. The article discusses hierarchy. But there is also progression, root cause, scope, and basically any correlation/causation relationship ML/AI figures out.

- They let us directly manipulate the nodes & edges, such as for drilling down, navigating, reclustering, etc.

- A useful 'aha' is thinking of modern information visualization as trying to optimize some sort of time-to-insight through a sequence of visual interactions. So each view must be information dense for visually revealing certain insights, and make it easy to get to the next set of visual Q&A.

- Ex: When the entities are the interesting thing wrt questions, being able to drill down into individual nodes/edges into great dedicated views becomes important, so graphs get to need to be multimodal. And if the relationship aspect is unimportant... then graph view hurts more than it helps.

- From optimization perspective, it now makes sense to specialize for specific domains. Maybe what is needed is more of a small diagram, and not actually investigating a lot of relationships. Or a graph of subway stops, which has additional visual considerations. For a website, a sitemap navigation vs clickstream product analytics view would likewise need

A good analogy is a map. Sometimes exploring Google Maps is great, and you drill into a business inspector sidebar or down to a street view. But other times, it's better to have the map embedded into Yelp.com restaurant entry when you just need a quick view of mapping information as part of some broader context. Or you don't care about that map at all and can skip it.

Given all that.. it's interesting to revisit asking... what is the 'content' job to be solved? What kinds of content lean towards graph, and which don't?

We regular use pygraphistry to generate /import => viz 100k+ entity embeddings on mobile fine: https://github.com/graphistry/pygraphistry

More fun, in umap mode, by default, it also shows the top-n similarity edges between each entity, so you get an interactive graph you can recluster, vs just the 2d scatter plot

We make it pretty easy to go from networkx or any other pydata (DF, csv, parquet, ...) to interactive GPU viz w all sorts of analytics built in: https://github.com/graphistry/pygraphistry#explore-any-data-...

I appreciate the good faith attempt:

https://github.com/graphistry/pygraphistry

And yes, we currently get used by data scientists and devs on problems like supply chain analysis, misinformation, cybersecurity, human trafficking. Seeing 100x+ more data than d3 and having a full env there makes their investigations easier. Our original tech helped lead to what is now Apache Arrow (we wrote the JS tier) and Nvidia RAPIDS (we wrote the precursor in js/opencl, and worked with Nvidia to restart for pydata), and are now focusing on the Nvidia Morpheus & graph AI sides for end-to-end GPU pipelines with our bigger customers (cyber, ...). To make this kind of tech easier for analysts, who are traditionally stuck with Splunk/Kibana/etc style UIs for investigations, we have been launching louie.ai with various customers. L

Hopefully now it makes sense why we don't go far with candidates who can't have conversations on these things.

This! We do it all the time in fraud, genomics, social media, security, etc

We do one more thing: connect the nearest neighbors to make an interactive similarity graph. Takes just a few lines in total: https://github.com/graphistry/pygraphistry/blob/master/demos...

It's super useful in practice!

In the table -> hypergraph transform @ https://github.com/graphistry/pygraphistry , we do `hypergraph(multicolumn_table, direct=True | False)['graph'].plot()` , which renders hypergraphs as a regular graph, this lets you pick/. Consider exploring some logs of customer activity or security events:

A hyperedge becomes either:

- a node of a bipartite graph. Ex: each log event becomes a node connecting the various entity nodes it mentions (IPs, accounts, countries, ...)

- .. or a bunch of pairwise entity<>entity edges. Ex: connect each IP<>account<>country directly, and label each edge with the hyperedge it came from.

In both cases, you can now directly leverage a lot of traditional graph thinking, and in our case, GPU acceleration.

Other systems might render hyperedges as say circles encomposing their nodes, but that's trickier at even small/medium scales

I increasingly just directly equate 'logs' with 'hypergraphs' and skip the relational step :)

Built from this data... https://github.com/networkx/networkx/blob/main/examples/grap...

I think one of the elements that author is missing here is that graphs are sparse matrices, and thus can be expressed with Linear Algebra. They mention adjacency matrices, but not sparse adjacency matrices, or incidence matrices (which can express muti and hypergraphs).

Linear Algebra is how almost all academic graph theory is expressed, and large chunks of machine learning and AI research are expressed in this language as well. There was recent thread here about PageRank and how it's really an eigenvector problem over a matrix, and the reality is, all graphs are matrices, they're typically sparse ones.

One question you might ask is, why would I do this? Why not just write my graph algorithms as a function that traverses nodes and edges? And one of the big answers is, parallelism. How are you going to do it? Fork a thread at each edge? Use a thread pool? What if you want to do it on CUDA too? Now you have many problems. How do you know how to efficiently schedule work? By treating graph traversal as a matrix multiplication, you just say Ax = b, and let the library figure it out on the specific hardware you want to target.

Here for example is a recent question on the NetworkX repo for how to find the boundary of a triangular mesh, it's one single line of GraphBLAS if you consider the graph as a matrix:

https://github.com/networkx/networkx/discussions/7326

This brings a very powerful language to the table, Linear Algebra. A language spoken by every scientist, engineer, mathematician and researcher on the planet. By treating graphs like matrices graph algorithms become expressible as mathematical formulas. For example, neural networks are graphs of adjacent layers, and the operation used to traverse from layer to layer is matrix multiplication. This generalizes to all matrices.

There is a lot of very new and powerful research and development going on around sparse graphs with linear algebra in the GraphBLAS API standard, and it's best reference implementation, SuiteSparse:GraphBLAS:

https://github.com/DrTimothyAldenDavis/GraphBLAS

SuiteSparse provides a highly optimized, parallel and CPU/GPU supported sparse Matrix Multiplication. This is relevant because traversing graph edges IS matrix multiplication when you realize that graphs are matrices.

Recently NetworkX has grown the ability to have different "graph engine" backends, and one of the first to be developed uses the python-graphblas library that binds to SuiteSparse. I'm not a directly contributor to that particular work but as I understand it there has been great results.

In the project we used Python lib networkx and a DiGraph object (Direct Graph). To detect a table reference in a Query, we use sqlglot, a SQL parser (among other things) that works well with Bigquery.

If you program in Python, can use NetworkX for that. But it's probably a good idea to implement the basic algorithms yourself at least one time.

For those wanting to play with graphs and ML I was browsing the arangodb docs recently and I saw that it includes integrations to various graph libraries and machine learning frameworks [1]. I also saw a few jupyter notebooks dealing with machine learning from graphs [2].

Integrations include:

* NetworkX -- https://networkx.org/

* DeepGraphLibrary -- https://www.dgl.ai/

* cuGraph (Rapids.ai Graph) -- https://docs.rapids.ai/api/cugraph/stable/

* PyG (PyTorch Geometric) -- https://pytorch-geometric.readthedocs.io/en/latest/

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1: https://docs.arangodb.com/3.11/data-science/adapters/

2: https://github.com/arangodb/interactive_tutorials#machine-le...

org-roam-ui is a great interactive visualization tool, but its main use is visualization. The hope of this library is that it could be part of a larger graph analysis pipeline. The demo provides an example graph visualization, but what you choose to do with the resulting graph certainly isn't limited to that. See for example networkx.