Connectedness: Indegree and Outdegree

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來自 University of California San Diego 的課程

Graph Analytics for Big Data

766 個評分

University of California San Diego

Graph Analytics for Big Data

766 個評分

課程 5（共 6 門，Specialization Big Data）

Want to understand your data network structure and how it changes under different conditions? Curious to know how to identify closely interacting clusters within a graph? Have you heard of the fast-growing area of graph analytics and want to learn more? This course gives you a broad overview of the field of graph analytics so you can learn new ways to model, store, retrieve and analyze graph-structured data. After completing this course, you will be able to model a problem into a graph database and perform analytical tasks over the graph in a scalable manner. Better yet, you will be able to apply these techniques to understand the significance of your data sets for your own projects.

從本節課中

Graph Analytics

Connectivity Analytics3:30

Disconnecting a Graph1:45

Connectedness: Indegree and Outdegree3:21

與講師見面

Amarnath Gupta
Director, Advanced Query Processing Lab
San Diego Supercomputer Center (SDSC)

We have already defined the degree of the node

as the number of edges connected to it.

Thus specifying if a node is more connected than another.

Looking closer we can separate out

that two components denotes degrees into in degree and out.

Which are the counts of the incident and the outgoing edges of a node respectively.

In the example graph G has an indegree and

outdegree of three making the total degree equal to six.

We first construct this degree table for each node.

It's a simple procedure where we count the number of nodes with degree.

0, 1, 2, and so forth.

The degree versus count table is a degree histogram of the graph.

We can compare two graphs by computing the vector distance between them.

One simplistic measure is just the Euclidean distance.

For our case, the degree histogram based on comparisons of

the histograms find the graphs to be very similar.

The more sophisticated methods are available but

are outside the scope of this course.

We can also compute histograms of just the in degree or

just the out degree of the graph.

But perhaps more interesting is the joint two dimensional histogram of the graph,

the colorful histogram of the graph can be interpreted here as follows.

The graph has a maximum in degree of three and a maximum out degree of three.

This creates a two-dimensional histogram with four times four

equal to 16 different joined values.

The actual value for any combination is computed from the graph and

color coded in the ratings.

For example, there is no node with in degree 0 and out degree 0.

So the lower-left square of the graph has value zero and color-coded blue.

On the other hand, there are two nodes with in-degree three and out-degree three.

Thus, the top-right corner has the value twp, which is 20% of the nodes.

Color coded as light green.

The 2D histogram provides an interesting insight.

The nodes with more incident edges than outgoing edges

represent entities that take in more than they put out.

In a social networking setting, they represent members who are listeners.

They receive a lot of posts, but send much fewer posts.

On the opposite side of the spectrum,

there are talkers whose out-degrees exceed their in-degrees.

The entities that are in between, we have both large values of in-degree and

out-degree.

These are communicators.

In this graph, there seems to be more talkers than listeners.

Not surprising though.

My social media friends show similar statistics.

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