Hello guys.
So in this section of the video we are going to discuss about Neo4j's property Graph data model.
Now this Neo4j's graph database
uses something called as property graph model property graph model.
And this model is specifically used to store.
And manage data.
Okay.
So we'll get an idea about it as we go ahead as we proceed okay.
Now this is very much important.
What exactly is property graph model.
Now this model represents three main important thing.
One is nodes.
Second one is something called as relationships.
And the third is nothing.
But let me write it in another color so it looks good.
So the first is nothing.
But over here I will go ahead and write.
It is nothing but nodes.
notes.
Second is relationships.
Third is something called as properties.
So this property graph model or this model represents the data, the entire data, whatever data you
have right over here in this three important in in in in specifically this three important things that
is nodes relationships and properties.
Properties are just like rows and records.
So this will specifically be having key value pairs.
Okay.
It will be having key value pairs.
And one more thing that you really need to understand when we talk about this relationship.
This is both uni directional.
And bi directional.
So whenever we go ahead and create any kind of relationship, you can actually create it in the form
of uni directional.
And you can also create it in the form of bi directional okay.
And every relationship okay.
Every relationship.
See if I probably consider this relationship, let's say this is my node one.
This is my node two.
Right.
So let's go ahead and write node one.
Node two okay.
Now with respect to this node one and node two, whenever we create any relationship let's say this
is having a relationship of this.
Let's say node one is the parent.
Or I can go ahead and write node one is the parent of node two.
Right now in this relationship.
This node one is just like your start node.
And if I probably consider this node two, it is nothing but it is my two node.
Or it can we can also say two or uh end node.
Okay.
Since we are basically going to write this right.
See this is this is also called a start node.
It can also be called as something called as from node okay.
This can also be called as two node.
So this is really important for you all to understand okay.
But if I probably consider the entire graph DB data model.
So if I probably consider graph DB data model.
Okay.
Here there are three important blocks okay.
One is nodes.
One is relationship.
And the third one is something called as properties.
Okay.
As I said, uh, I've given you multiple examples over here.
So let's say this is one of my node.
This is my another node.
This is my third node.
Okay.
Here, uh, I will go ahead and take one movie.
Right.
So let's say Avenger okay.
So let's say Avenger and Tony Stark okay.
So I will say hey Tony Stark.
Tony Stark was an actor in Avenger, right?
So this is one kind of relationship.
Okay.
Um, like, Tony Stark was an actor in Avenger.
They can be another movie where Tony Stark was again an actor.
Right here.
I guess he was a villain.
I'm just putting.
Okay.
There was a villain relationship with respect to one of the movie.
This movie can be any other movie, right?
Movie two.
So this way you'll be able to see at any point of time this pink color node.
This pink color circle is basically called as node.
This is called as relationship.
Relationship.
Right.
This actor is basically a kind of uh, you can just consider that it is a kind of property, but we
can define this property in key value pairs also, which I will probably be showing you.
Right.
So, uh, this is what is entirely, uh, you know, the understanding of the graph, uh, node go for
sorry Neo4j's property graph data model.
And again, uh, I said you right.
This can be bidirectional.
Also it can be a relationship.
It can be unidirectional.
It can be bidirectional.
Okay.
So like this, uh, if we have an entire NLP text data, right.
Just imagine an NLP text data then just by using.
And this internally uses something called as graph neural network.
With the help of this graph neural network we will be able to create this right now from where we will
be getting this graph neural network.
For this we will be using link chain.
Okay.
Um again, Lang Chin has a good graph DB functionalities which will be able to take that particular
text data.
And uh, we will be able to convert this entire into this particular nodes.
Okay.
But for that again we right.
We need to write Cypher query.
But if you just directly want to convert it with the help of model we can use a graph neural network.
And I think graph neural network uh functionalities this also you can probably go ahead and train it
from completely scratch.
But right now we'll focus more on to the long chain, uh, which has features to convert this particular
data insert into a graph DB database itself.
So yeah.
Uh, this was all about graph data model.
Now, uh, in the next section of the video, we will go ahead and do some variety of practicals and
we'll try to insert some data in our Neo4j's RDB.
So yes, this was it.
I will see you all in the next video.