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Pygraphistry Alternatives
Similar projects and alternatives to pygraphistry
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Scout Monitoring
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reddit-detective
Play detective on Reddit: Discover political disinformation campaigns, secret influencers and more
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InfluxDB
Purpose built for real-time analytics at any scale. InfluxDB Platform is powered by columnar analytics, optimized for cost-efficient storage, and built with open data standards.
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Splunk-Apps
Palo Alto Networks App for Splunk leverages the data visibility provided by Palo Alto Networks next-generation firewalls and endpoint security with Splunk's extensive investigation and visualization capabilities to deliver an advanced security reporting and analysis tool.
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SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better pygraphistry alternative or higher similarity.
pygraphistry discussion
pygraphistry reviews and mentions
Posts with mentions or reviews of pygraphistry.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-03-05.
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Graph Data Fits in Memory
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...
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The "missing" graph datatype already exists. It was invented in the '70s
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...
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Displaying Content as a Graph
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?
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NeurIPS 2023 Posters Cluster Visualization
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
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NetworkX – Network Analysis in Python
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-...
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How to pass any first-round interview (even in a terrible talent market)
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.
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Handbook of Graph Drawing and Visualization
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...
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Don't Bring a Tree to a Mesh Fight
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 :)
- An Engineer's View of Venture Capitalists (2011)
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A note from our sponsor - SaaSHub
www.saashub.com | 12 Sep 2024
Stats
Basic pygraphistry repo stats
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2,113
9.3
about 1 month ago
graphistry/pygraphistry is an open source project licensed under BSD 3-clause "New" or "Revised" License which is an OSI approved license.
The primary programming language of pygraphistry is Python.
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