SIOC Post profile for "AI3:::Adaptive Information"
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primary topic: CWPK #56: Graph Visualization and Extraction
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CWPK #56: Graph Visualization and Extraction
type: Post
has container: weblog
has creator: Mike Bergman
maker: Mike Bergman
created: 2020-10-21T16:04:52Z
content: Working Examples are Not so Easy to Come By Today's installment in our Cooking with Python and KBpedia series is a great example of how impressive uses of Python can be matched with frustrations over how we get there and whether our hoped-for desires can be met. The case study we tackle in this installment is visualization of the large-scale KBpedia graph. With nearly 60,000 nodes and about a quarter of a million edge configurations, our KBpedia graph is hardly a toy example. Though there are certainly larger graphs out there, once we pass about 10,000 nodes we enter difficult territory for Python as a performant language. We'll cover this topic and more in this installment. Normally, what one might encounter online regarding graph visualization with Python begins with a simple example, which then goes on to discuss how to stage the input data and then make modifications to the visualization outputs. This approach does not work so well, however, when our use case scales up to the size of KBpedia. Initial toy examples do not provide good insight how the various Python visualization packages may operate at larger scales. So, while we can look at example visualizations and example code showing how to expose options, in the end whether we can get the package to perform requires us to install and test it. Since our review time is limited, and we have to in the end produce working code, we need a pretty efficient process of identifying, screening, and then selecting options. Our desires for a visualization package thus begin with the ability to handle large graphs, including graph analytic components in addition to visualization, compatibility with our Jupyter Notebook interactive environment, ease-of-learning and -implementation, and hopefully attractive rendering of the final graph. From a graph visualization standpoint, some of our desires include: Attractive outputs Ability to handle a large graph with acceptable rendering speed Color coding of nodes by SuperType Vary…
encoded: Working Examples are Not so Easy to Come By Today’s installment in our Cooking with Python and KBpedia series is a great example of how impressive uses of Python can be matched with frustrations over how we get there and whether our hoped-for desires can be met. The case study we tackle in this installment is visualization of the large-scale KBpedia graph. With nearly 60,000 nodes and about a quarter of a million edge configurations, our KBpedia graph is hardly a toy example. Though there are certainly larger graphs out there, once we pass about 10,000 nodes we enter difficult territory for Python as a performant language. We’ll cover this topic and more in this installment. Normally, what one might encounter online regarding graph visualization with Python begins with a simple example, which then goes on to discuss how to stage the input data and then make modifications to the visualization outputs. This approach does not work so well, however, when our use case scales up to the size of KBpedia. Initial toy examples do not provide good insight how the various Python visualization packages may operate at larger scales. So, while we can look at example visualizations and example code showing how to expose options, in the end whether we can get the package to perform requires us to install and test it. Since our review time is limited, and we have to in the end produce working code, we need a pretty efficient process of identifying, screening, and then selecting options. Our desires for a visualization package thus begin with the ability to handle large graphs, including graph analytic components in addition to visualization, compatibility with our Jupyter Notebook interactive environment, ease-of-learning and -implementation, and hopefully attractive rendering of the final graph. From a graph visualization standpoint, some of our desires include: Attractive outputs Ability to handle a large graph with acceptable rendering speed Color coding of nodes by SuperType Vary…
topic: CWPK
topic: KBpedia
topic: Semantic Web Tools
links to: Cooking with Python and KBpedia
links to: Python
links to: KBpedia
links to: CWPK #8
links to: bokeh palettes
links to: cb1-1
links to: cb1-2
links to: cb1-3
links to: CWPK #49
links to: owlready2
links to: cb2-1
links to: cb2-2
links to: cb2-3
links to: cb3-1
links to: cb3-2
links to: cb3-3
links to: cb3-4
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links to: cb3-56

Mike Bergman
type: User

Mike Bergman
type: Person

Cooking with Python and KBpedia
label: CWPK listing

Cytoscape

written about it

Gephi

written about it

PyViz

benchmark comparisons of network packages

NetworkX

SNAP

NetworkKit
label: NetworKit

igraph

graph-tool

Lightgraphs

Julia

graphViz

matplotlib

Windows 10 installation path

Linux subsystem layer

HoloViews

hvPlot

KBpedia

code listing covering the series

direct download

notify me