Neo4j Graphs: Creating and Querying Edges and Nodes

First it is helpful to understand the basic premise behind Graph Theory, which is the foundation of these "Pairwise relations between objects" that we can store and explore in graph databases. The mathematical representation is the formula:

A graph (G) is equal to the connections of its entity nodes aka "vertices" (V) with its relationship edges (E)

Swiss mathematician Leonhard Eueler was looking for a solution to a puzzle on the relationships between land masses and bridges in the Prussian city of Konigsberg. This was the genesis of Graph Theory:

"The geometry of position, now known as Graph theory"

Graph database technology is useful for analyzing complex relationships and relationship behaviors in myriad scenarios not limited to:

• Computer Networking
• Spread of Gossip
• Fraud Detection
• Forensic Investigations
• Flight Mapping/GPS
• Population Growth
• Spread of Infectious Disease
• Hierarchy Visualization

It is also vital component for the analysis of (increasingly) unstructured data. By unstructured we typically mean data that is not easily modeled in a traditional relational hierarchy but may still have common properties and so still have relationship value. It is estimated that around 80-90% of an organization's data is unstructured.

Example of a couple simple graph relationships

Relationships in graph data are formed by the edge tables which signify a relationship between two different entities or "nodes" which are stored in node tables. Instead of RDBMS FK/PK and other check constraints, the relationships are defined in the edge tables; all of the properties that one would want to query via CQL to find things are stored in JSON metadata inside the node tables instead of the columns-for-each-property that defines relational data architecture.

Node Tables: Represent a data entity

Edge Tables: Store relationships between nodes

For the RDBMS purists and skeptics out there, I recommend this quote about Node, Graph and other alternative data processing paradigms vs. the traditional RDBMS OLTP and OLAP models:

"The NOSQL acronym is: Not Only S Q L. NOSQL solutions are not a replacement or successor for RDMBS systems, nor were they ever intended to be. They are useful tools to be used for specific purposes. They are to be used as part of an organisation’s data management solution and not as a total replacement for the existing solution". -Simon Munro

It is important to note that graph data can be queried in much the same way as SQL through a graph-specific query language called CQL (cipher query language) as you will see in the following demonstration.

For a quick demonstration on how easy it is to get up and running with this technology we will be using Neo4j which you can download here: https://neo4j.com/download/


Walkthrough of Neo4j:
Fire up the Neo4j Desktop client, click "New" and then click the "Add Graph" button to create your first graph database. Once the db has been created, click the play icon to start it up (it must be running for Neo4j browser to connect).

This is the Neo4j main home screen from which you can create and connect to graph databases and projects

Then click the "Neo4j Browser" button to launch the graph data browser for creation and visual exploration of graph data relationships.

Next, in the Neo4j browser, click "Jump into Code" and simply follow the prompts to begin creation and querying of graph data.

Once, in the Neo4j tutorial, you can simply follow the prompted instruction widgets.

I would continue with instructions but the rest is refreshingly self-explanatory. You first create a movie database that contains actors, directors and movies stored in Node (entity) tables and the types of relationships between these Nodes stored in Edge (relationship type) tables.

One thing to note is that to execute a CQL command in the browser, you must hold down CTRL+Enter. Alternatively you can click the play button to execute CQL.

As you continue with the tutorial you will wind up with something like this when you get to the step that has you execute CQL to find all Node entities that are within 4 degrees (or node "hops") of the actor Kevin Bacon:

4 degrees of separation from Kevin Bacon...

Once you get comfortable with CQL syntax it is relatively easy to start modeling and creating your own graph database structures which can help you and/or your company to analyze some of the unstructured and semi-structured data that is hard to extract value from with traditional RDBMS.

Bigtime kudos to the Neo4j team on making this so straightforward and simple to learn and get up
and running with a new technology so fast. I've never seen a technology tutorial like it.

As you can see, there is tremendous potential value in exploring data relationships that don't necessarily fit neatly into traditional RDBMS/hierarchical databases but are no less useful a tool to have in an organization's data analysis arsenal.


References:

https://www.mssqltips.com/sqlservertip/5007/sql-server-2017-graph-database-query-examples/

https://www.youtube.com/watch?v=gXgEDyodOJU

https://www.red-gate.com/simple-talk/sql/t-sql-programming/experiments-with-neo4j-using-a-graph-database-as-a-sql-server-metadata-hub/

https://www.youtube.com/watch?v=mVWn8k49mAQ

Securitization

Securitization is the creation and sale of pieces of debt from a pool of similar debt assets. It is a way for banks to take a group of home mortgage loans for instance, and cut the asset group into pieces or "tranches" that can be sold as MBSs (mortgage backed securities) on the open market.

Lots of touch points in this interesting "value abstraction" process

While many investment banks who used this financial implement in the run-up to the Great Recession have been strongly criticized for not vetting assets thoroughly enough in the origination process, the process of securitization will always be a method for asset holders to convert an illiquid asset like a group of home mortgages or consumer credit card debt into something (or rather "some things") that can be more easily packaged, bought and sold on the open market.


Reference: https://blog.bankex.org/paving-the-way-from-securitization-to-tokenization-ac0187ba6d48

Options, Calls and Puts

In finance, an option is a contract which gives the buyer the right, but not the obligation, to buy or sell an underlying asset at a specified price prior to or on a specified date, known as the "expiry date". An option contract typically requires an upfront payment for the option, called the premium.

A call option, also referred to as a "call" in finance jargon, gives the buyer the right to buy the underlying asset at an agreed-upon price on a specific date or within a specified period of time.

A put option, also referred to as a "put", gives the buyer the right to sell the underlying asset at an agreed-upon price on a specific date or within a specified period of time.

Calls give the right to buy, puts give the right to sell

The important characteristic of options contracts is that they give the right- not the obligation- to buy or sell an asset at some agreed upon price on or before the option's contract expiration date. The option holder can simply walk away from the option to buy or sell if she or he decides it is no longer in their best interest.

Options are another asset class, and when used correctly, they offer many advantages that trading stocks and ETFs alone cannot (namely the ability to decide not to exercise the option if the value of the underlying asset being bought or sold changes significantly (in the wrong direction) for the option holder before the expiry date, for instance).

Options are different from futures contracts in that option contracts give the right to buy or sell on or before some date, while futures contracts represent an obligation to buy or sell on some date.

With options, financial traders can lock in future gains if an asset value is expected to (and does) rise in value above their call price, and conversely can stem future losses if an asset value is expected to (and does) drop in value below their put price.


References:

https://investinganswers.com/financial-dictionary/optionsderivatives/option-2049

https://www.fool.com/investing/options/options-the-basics.aspx