Some Basic and Useful Cypher Queries for Neo4J
Steven Haines concludes his three-part series on Neo4j with an For this example, we'll create a simple movie catalog that has the following types of nodes: . and then find all nodes that have a HAS_TAG relationship with both of Programming Skills for Data Science: Start Writing Code to Wrangle. Aside: Your Cypher query uses node labels and relationship types that do not match your illustrated data model. So, I will ignore your illustration. This is a. return distinct labels(n), type(r). Find nodes that don't have any relationships: start n=node(*) match n-[r?]-() where r is null return n. Find all nodes that have a.
Types of interaction between organism A and organism B.
More information about biological interactions can be found here. Modeling weighted networks as energy exchanges between nodes is just one of the insights Hugo derived from connecting biology and information management. We are very excited about the analytical possibilities of his model choice since it allows us to distinguish the effect of a relationship on source and target nodes and grasp the complex dynamics of a system. These analytical possibilities, such as the use of color coding of relationships based on specific criteria, can provide important systemic insights.
For example, selective relationship coloring is used in the figure below to reveal strong or weak spots in an ecosystem map. Graphs are by definition cross-boundary constructs and are well suited solutions for many new use cases coming from these intersections. The Semantics Of Connected Data Another important intersection is between linguistics and technology. Graph technology is again a core player in this game, as meaning and form in language are also dependent on the connections between speech elements, context, and other concepts.
Bubbles, as units of information, are associated with Concepts.
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One bubble has one main concept the topicbut is also linked to other concepts through tags. This way we have interconnected Presentations, and we can easily find them with simple Cypher queries like this: Using Natural Language Processing NLP we could easily detect when two bubbles refer to the same concept, even with language or spelling variances.
Automatic categorization of Presentations is possible as well. We can also leverage these semantic connections of the data to provide recommendations on which tags to add to a bubble or even suggest which bubble should be next in a presentation.
PowerPoint-like tools are often used to create presentations, including different views of the same content. By taking advantage of NLP and graphs, the system could detect what you are working on and, after a few initial bubbles, could suggest the next topic. Such systems would function as a PowerPoint where pieces of information can be populated on demand, such as making topic or tag selections from a suggestions list. The free version is publicly available and is called Lucky7 as it allows the creation of presentations of up to 7 bubbles.
The bubbles are positioned according to their number, generating a beautiful crystal-like presentation of a central topic and its branches. Previous and next buttons allow sideways navigation making it easy for the presenter to explore the bubble-verse.NoSQL: Some theory about Neo4j Relationships
The full-screen mode allows you to examine bubble media, such as uploaded images or embedded YouTube videos, in more detail. As an exampleFigure 7 illustrates a presentation about visual complexity.
The Ecosystem Map contains numerous advanced features and does not impose any limits on the number of graph elements. Some of these advanced features empower the storytelling and content creation experience, such as in-place editing. The editing view is embedded into the visualization, so fields are editable and drag-and-drop gestures allow to add bubbles and links to the graph.
Drag and drop to create new bubbles or new links. When we tell a story, such as during a presentation, we often wish to adjust the content to the audience reactions and to the performance of the moment.
7VORTEX: Systems Thinking Powered by Graph Technology
We want the audience to follow the thread of the story, sometimes allowing them to jump in. A pre-defined linear sequence, showed one slide at a time, is often not dynamic enough.
Instead, stories can be seen as a networks of connected chapters where one scene leads to another. The 7VORTEX Presentation is actually a graph and not a simple hierarchy or sequence of slides, allowing us to experiment with new graph-based storytelling features, such as breadth-first and depth-first narrative modes.
These modes will allow the presenter to navigate the presentation, moving to or revealing adjacent bubbles according to the graph topology. The application currently uses a force simulation layout with collision detection, capable of automatically displaying graphs.
More information about constraints and their effect on indexes is elaborated in the Neo4j documentation on constraints.
7VORTEX: Systems Thinking Powered by Graph Technology
Assuming that Neo4j is started with an appropriate Database Location selectedwe should first see an empty graph. This means that there are no nodes or relationships. Our goal is to populate the graph with the data from the JSON dataset by defining its skeleton nodes and relationships. To accomplish this, we will use the concept of user-defined procedures in Neo4j. These procedures are specific functionalities which can be re-used to manipulate the graph. To do this, open your project and click on the Manage button.
Getting Started with Data Analysis using Neo4j [Community Post]
Next, click on the Plugins tab. Under this tab, you will see the APOC heading accompanied with its version and a description. Click on the Install and Restart button. Here is a screenshot of a successful APOC installation: More information about user-defined procedures and the APOC library installation, usage, examples is elaborated in this Neo4j blog article: We are interested in a specific procedure — apoc.
This procedure will return a singular map if the result is a JSON object or a stream of maps if the result is an array. The following code snippet illustrates the use of the apoc. However, we first need to parse this bulk JSON data. At this stage, we are required to possess an understanding of the structure of the JSON data. As mentioned in step 1, we can access the entries using the key items.
This is illustrated as: Now, we can use the API to access the values. Thus, we are required to explicitly select those entries which possess the attribute. The following code snippet illustrates this: Now that we have access to the values of each entry, it is time to create the nodes and relationships.
For example, if we execute the following statements sequentially provided that the constraints were not created: This can be illustrated as follows: The final Cypher query consisting of all the above components will look like this: Added labels, created nodes, set properties, created relationships, statement executed in ms.
The output can be visualized as follows: