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Hello guys.

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So in this section of the video we are going to discuss about Neo4j's property Graph data model.

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Now this Neo4j's graph database

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uses something called as property graph model property graph model.

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And this model is specifically used to store.

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And manage data.

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Okay.

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So we'll get an idea about it as we go ahead as we proceed okay.

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Now this is very much important.

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What exactly is property graph model.

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Now this model represents three main important thing.

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One is nodes.

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Second one is something called as relationships.

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And the third is nothing.

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But let me write it in another color so it looks good.

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So the first is nothing.

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But over here I will go ahead and write.

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It is nothing but nodes.

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notes.

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Second is relationships.

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Third is something called as properties.

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So this property graph model or this model represents the data, the entire data, whatever data you

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have right over here in this three important in in in in specifically this three important things that

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is nodes relationships and properties.

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Properties are just like rows and records.

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So this will specifically be having key value pairs.

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Okay.

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It will be having key value pairs.

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And one more thing that you really need to understand when we talk about this relationship.

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This is both uni directional.

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And bi directional.

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So whenever we go ahead and create any kind of relationship, you can actually create it in the form

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of uni directional.

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And you can also create it in the form of bi directional okay.

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And every relationship okay.

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Every relationship.

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See if I probably consider this relationship, let's say this is my node one.

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This is my node two.

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Right.

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So let's go ahead and write node one.

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Node two okay.

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Now with respect to this node one and node two, whenever we create any relationship let's say this

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is having a relationship of this.

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Let's say node one is the parent.

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Or I can go ahead and write node one is the parent of node two.

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Right now in this relationship.

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This node one is just like your start node.

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And if I probably consider this node two, it is nothing but it is my two node.

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Or it can we can also say two or uh end node.

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Okay.

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Since we are basically going to write this right.

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See this is this is also called a start node.

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It can also be called as something called as from node okay.

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This can also be called as two node.

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So this is really important for you all to understand okay.

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But if I probably consider the entire graph DB data model.

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So if I probably consider graph DB data model.

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Okay.

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Here there are three important blocks okay.

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One is nodes.

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One is relationship.

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And the third one is something called as properties.

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Okay.

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As I said, uh, I've given you multiple examples over here.

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So let's say this is one of my node.

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This is my another node.

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This is my third node.

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Okay.

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Here, uh, I will go ahead and take one movie.

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Right.

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So let's say Avenger okay.

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So let's say Avenger and Tony Stark okay.

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So I will say hey Tony Stark.

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Tony Stark was an actor in Avenger, right?

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So this is one kind of relationship.

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Okay.

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Um, like, Tony Stark was an actor in Avenger.

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They can be another movie where Tony Stark was again an actor.

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Right here.

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I guess he was a villain.

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I'm just putting.

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Okay.

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There was a villain relationship with respect to one of the movie.

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This movie can be any other movie, right?

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Movie two.

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So this way you'll be able to see at any point of time this pink color node.

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This pink color circle is basically called as node.

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This is called as relationship.

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Relationship.

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Right.

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This actor is basically a kind of uh, you can just consider that it is a kind of property, but we

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can define this property in key value pairs also, which I will probably be showing you.

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Right.

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So, uh, this is what is entirely, uh, you know, the understanding of the graph, uh, node go for

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sorry Neo4j's property graph data model.

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And again, uh, I said you right.

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This can be bidirectional.

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Also it can be a relationship.

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It can be unidirectional.

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It can be bidirectional.

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Okay.

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So like this, uh, if we have an entire NLP text data, right.

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Just imagine an NLP text data then just by using.

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And this internally uses something called as graph neural network.

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With the help of this graph neural network we will be able to create this right now from where we will

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be getting this graph neural network.

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For this we will be using link chain.

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Okay.

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Um again, Lang Chin has a good graph DB functionalities which will be able to take that particular

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text data.

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And uh, we will be able to convert this entire into this particular nodes.

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Okay.

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But for that again we right.

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We need to write Cypher query.

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But if you just directly want to convert it with the help of model we can use a graph neural network.

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And I think graph neural network uh functionalities this also you can probably go ahead and train it

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from completely scratch.

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But right now we'll focus more on to the long chain, uh, which has features to convert this particular

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data insert into a graph DB database itself.

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So yeah.

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Uh, this was all about graph data model.

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Now, uh, in the next section of the video, we will go ahead and do some variety of practicals and

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we'll try to insert some data in our Neo4j's RDB.

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So yes, this was it.

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I will see you all in the next video.