Vital AI Cytoscape App in App Store

We recently published our Cytoscape App to the Cytoscape App Store.

Cytoscape is a wonderful graph visualization tool that is open-source, available on Desktops, and quite handy for graph analysis and visualization.

Our plugin allows Cytoscape to connect to databases, servers, and Apache Spark/Hadoop using the VitalService API.

The plugin is available directly in Cytoscape, or here: http://apps.cytoscape.org/apps/vitalaigraphvisualization

Cytoscape is available here: http://cytoscape.org/

Prior to using the plugin, the Vital AI software must be installed and configured.  The Vital AI software can be downloaded from here: http://vital.ai/#download

The Cytoscape plugin uses the VITAL_HOME environmental variable to find the Vital AI software and configuration files.

For those using Mac OSX, OSX needs some extra help for desktop applications like Cytoscape to use environmental variables.

Here is a good StackOverflow answer which helps Mac OSX use environmental variables: http://stackoverflow.com/a/32405815/2138426

The first tab in the Vital AI plugin enables selecting which VitalService endpoint to connect to.  These come from the VitalService configuration file found at:

$VITAL_HOME/vital-config/vitalservice/vitalservice.config

For Prime endpoints, an authorization key is used to connect.  For convenience, you can put such keys into your vitalsigns configuration file, like so:

config: {

local-key: key1-key1-key1  

}

The naming convention is: “vitalservicename”-key

So, the corresponding VitalService configuration entry would be:

profile.local {

type = VitalPrime

appID = analytics

VitalPrime {

endpointURL = “http://127.0.0.1:9081/java”

}

}

 

Now back to the plugin…

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Here is the Connection tab.  Select the desired VitalService endpoint from the drop-down and hit “Connect”.

Now that we have connected, we can use the “Search” tab to search.

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We can select which databases (“Segments”) to include, as well as what property to search.  In this case the “Wordnet” database is selected and the “name” property.

Let’s put all the results into a network.

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Now let’s select them all and find what is connected to them.  This is called an “expansion” query and looks for everything connected to the starting node up to two hops (edges) away.

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Starting the expansion…

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Expanding all the selected nodes…  The “Paths” tab is used to select whether the expansion will be for one hop (one edge) or two hops (two edges), the direction of the desired edges (forward, backward, or both), and which “Segments” to include in the expansion.

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And now we have some results!

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Let’s zoom in on part of the network.  We can then further analyze the results, continue to explore and expand the network, or tune the visualization.

 

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If we are connected to a Prime endpoint, we can use the Paths tab to select the node and edge types to filter with during an expansion query.

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Also with a Prime endpoint, we can use the “DataScripts” tab to run datascripts on the server.  Datascripts can be used to analyze data, trigger Spark or Hadoop jobs, use a prediction model, or anything you like.

Please send along any comments or questions, and hope you enjoy using Cytoscape and our plugin to visualize your data.

 

Data Visualization with Vital AI, Wordnet, and Cytoscape

In this series of blog posts, I’ll provide an example of using the Vital AI Development Kit (VDK) for Data Visualization.

One of my favorite visualization applications is Cytoscape ( http://www.cytoscape.org/ ).  Cytoscape is often used in Life Science research applications, but can be used for any graph visualization need.  I highly recommend giving it a whirl.  In this example, we’ll create a plugin to Cytoscape to connect with the Vital AI software.

Wordnet is a wonderful dataset that captures many types of relationships among words and word categories, including relationships like “part-of” as in “hand is part-of an arm” and “member-of” as in “soldier is member-of an army”.  Wordnet was developed at Princeton University ( http://wordnet.princeton.edu/ ).

Because Wordnet contains relationships between words, it’s an ideal dataset to use for graph visualization.  The technique can be applied to many different types of data.

For this example, we will:

  • Generate a dataset using the source Wordnet Data ready to load into Vital AI
  • Create a plugin to Cytoscape to connect to the Vital AI software via the VDK API
  • Visually explore the Wordnet data, perform some graph analysis, and use the analysis output as part of our visualization.

Once complete, the Cytoscape interface viewing the Wordnet data via the underlying Vital AI VDK API will look like:

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Next Post: https://vitalai.com/2014/04/29/generating-a-wordnet-dataset-using-vital-ai-development-kit/

Building a Data Visualization Plugin with the Vital AI Development Kit

Part of a series beginning with:

https://vitalai.com/2014/04/29/data-visualization-with-vital-ai-wordnet-and-cytoscape/

With the last post, we have a dataset generated from Wordnet according to our data model, imported into a Vital Service Endpoint, which could be on our local machine or available remotely. For simplicity, we’ll assume it’s a local endpoint.

Cytoscape 3.X (the current version) allows creating “apps” (formerly known as “plugins”) that allow adding additional functionality to the Cytoscape desktop application.

The documentation for creating such apps can be found here: http://wiki.cytoscape.org/Cytoscape_3/AppDeveloper

Cytoscape supports the OSGI standard ( http://en.wikipedia.org/wiki/OSGi ), which can be a little tricky. However, it provides a general way to include dependencies.

The source code for the Vital Service Cytoscape App is found on github here: https://github.com/vital-ai/vital-cytoscape

The two main implemented functions are: (1) searching the Wordnet data for a matching SynsetNode, and (2) given a particular Wordnet SynsetNode, performing a graph query to add all connected Edges and Nodes into the current graph.

For the first case, the “search” function is run in the SearchTab to produce a set of matching SynsetNodes. The snippet of code in SearchTab is:

ResultList rs = Application.get().search(selectquery);

For the second case, the “Expand Node” function is run when it’s selected by using the contextual menu on a Node (or set of Nodes).  The snippet of code in ExpandNodeTask is:

ResultList rs_connections = Application.get().getConnections(uri_string);

Within the Application class, we connect to VitalService and perform the select query, returning the found objects:

ResultList rlist = Factory.getVitalService().selectQuery(sq);

Within the Application class, we connect to VitalService and “expand” a graph object, triggering getting all the connected Nodes and Edges into the local cache, which we can then display.

GraphObject graphObjectExpanded = Factory.getVitalService().getExpanded(VitalURI.withString(uri_str), getWordnetSegment())

The Vital AI Cytoscape App adds some tabs and contexual menus to the Cytoscape User Interface.

Here’s the Search Tab displaying search results:

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and here is the contextual menu associated with a SynsetNode, used to trigger “expanding” the Node:

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Put it all together, and we can explore graph data stored in a Vital AI Endpoint using our new Cytoscape App!

Next we’ll use some Graph Analytics to help visualize our data.

Next Post: https://vitalai.com/2014/04/29/visualizing-data-with-graph-analytics-with-the-vital-ai-development-kit/

Visualizing Data with Graph Analytics with the Vital AI Development Kit

Part of a series beginning with:

https://vitalai.com/2014/04/29/data-visualization-with-vital-ai-wordnet-and-cytoscape/

In the previous post, we created a Cytoscape App connected to a Vital Service Endpoint containing the Wordnet dataset. The App can search the Wordnet data and “expand” it by adding connected Nodes and Edges to the visualized network.

Now let’s use some graph analytics to help visualize a network. We’ll be performing the analysis locally within Cytoscape. For a very large graph we would be using a server cluster to perform the analysis. The Vital AI VDK and Platform enable this by running the analysis within a Hadoop Cluster. However, for this example, we’ll be using a relatively small subset of the Wordnet data.

First let’s search for “car” (in the sense of “automobile”), add it to our network, and expand to get all nodes and edges up to 3 hops away. This gives us about 1,100 Nodes and around 1,500 Edges. Initially they are in a jumble, sometimes called a “hair-ball”.

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Now, let’s run our network analysis, available from the “Tools” menu.

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By doing the network analysis, we calculate various metrics about the network, such as how many edges are associated with each node — this is it’s “degree”.  Another such metric is called “centrality”.  This is a calculation of how “central” a node is to the network.  Central Nodes can be more “important” such as influencers in a social network.

Next, we associate some of these metrics with the network visualization.  We can adjust node size to the degree and color to centrality.  The more red a node is, the more “important” it is.

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We use the centrality associated with the edges to help visually lay out the network, showing some underlying structure, using options in the “Layout” menu.

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Next we can zoom in on the middle of the network.

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The node representing “car, automobile” is a deep red as it is the most central and important part of the graph.

Panning around, we can find “motor vehicle” —

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“Motor Vehicle” is a reddish-yellow reflecting it’s importance, but not as important as “car, automobile”.

Panning over to “airplane” we see that it’s bright yellow, with it’s sub-types like “biplane” a bright green, reflecting that they are not central and not “important” by our metric.  This is not a surprise, as “airplane” is a bit removed from the rest of the “car, automobile” network — they do have “motor vehicle” in common, and “biplane” is even further removed.

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Cytoscape has many layout and visualization features, and paired with a Big Data repository via the Vital AI VDK, makes a compelling data analysis system.

Using the contextual menu also allows the App to be a great data exploration application to discover new ways the data is connected.

Hope you have enjoyed this series on integrating a Data Visualization application with Vital AI using the Vital AI Development Kit!