PageRank | Implementation of the PageRank algorithm | Learning library

I created a cluster on Google Cloud Platform having five linux based virtual machines (VM): one master and 4 workers. I ran ./start-master.sh on the master VM and ./start-worker.sh [external-master-IP:7077] on the worker VMs.

Now I want to simply run a Graphx example job, for example a PageRank algorithm that is already in Spark, using ./bin/spark-submit.

I know, I read the documentation, which says to run like this:

I have a dataframe central

Then I want to plot the pairwise relationships between the columns with sns.pairplot(central). Could you please explain why the process just runs forever? I tried on both my laptop and Colab, but the problem persists.

im studying right now and starting with reactjs and all that, i have to make a web page based in Game of thrones using an API, i recieve the api data and i can print in screen the img, name and age of the characters, but i need to sort them by their age.

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I'm still a beginner in programming. I was writing some code (C on Linux) to calculate the page rank of some example webpages. I'm using the google formula, which is here: http link

Here is the code I wrote:

Based on the question I asked last time: Applying PageRank to a topic hierarchy tree(using SPARQL query extracted from DBpedia)

As I currently got the PageRank value against the Regulated concept map. Toward the concept "Machine_learning", my currently code is below:

I have following variables in my dataset: Customer, Merchant, Age, gender, category, amount, fraud

I need to generate following neo4j features from it:

Degree, Pagerank, Community for merchant and customer as well. So, eventually something like this:

merchdegree, custDegree, CustPagerank, merchPageRank, merchCommunity, custCommunity

// Computing PageRank for placeholder nodes (This won’t work because this library has been deprecated)

CALL algo.pageRank('Placeholder', 'PAYS', {writeProperty: 'pagerank'})

// Community detection using label propagation

CALL algo.beta.labelPropagation('Placeholder', 'PAYS', {write:true, writeProperty: "community", weightProperty: "cnt"})

// Viewing the PageRank results (WIP)

#MATCH (p:Placeholder) RETURN p.id AS id, p.pagerank as pagerank ORDER BY pagerank DESC

However, this library has been deprecated. I would appreciate if someone can guide me how to get this features and what is the alternate code or library that need to be used for this.

Suppose i create the following directed Graph using networkx and perform the pagerank algorithm on it

I'm writing different basic algorithms to practice writing code in a pythonic manner. Below is a function that produces a basic pagerank score at step k for a given graph G:

I'm testing PageRank in a projected graph 'ns_reverse' where I've applied before Node Similarity. My dataset has initially two types of nodes 'Keywords' and 'Articles' that are linked by a relationship 'APPEARS_IN', like this:

Keyword-[APPEARS_IN]->Article

After applying Node Similarity, my projected graph has also a new relationship 'SIMILAR', like this:

Article-[SIMILAR]->Article

Now that I'm testing PageRank to mesure the importance of each 'Article' node, I getting 'None' for the nodes type 'Keyword' but I do not want 'Keywords' nodes to be mesured. Here is the code: