grand VS pygraphistry

Cypher is a super useful language for querying graph structures, but sometimes it's overkill to load a tiny graph into Neo4j or memgraph. We wrote this tool to act as an abstraction layer so you can query in-memory graph data -- or, using [Grand](https://github.com/aplbrain/grand), rewrite Cypher queries to run on SQLite dbs or even other graph databases that don't support Cypher out of the box. Hoping it'll be helpful to those in the network theory, graph ML, and data science communities!

Hey cool! I'm super curious to hear more about this. I _also_ wrote a pseudo-graph-database on DynamoDB (https://github.com/aplbrain/grand) :) It pretends it's a networkx.Graph, generally, but we also have a Cypher implementation on top of it.

Would love to chat more about this sometime if you were interested!

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 :)