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Page rank is an algorithm used to find the current node score based on its relationships with other nodes. It is mostly used for finding scores of papers in which paper citation score is found by seeing the papers that are connected to this paper(node) and by the papers(node) that are being connected to this paper.

Two things are important in finding the node score The outgoing links from a paper. The incoming links from the papers.

This is the data on which we want to apply page rank.

CREATE
(home:Page {name:'Home'}),
  (about:Page {name:'About'}),
  (product:Page {name:'Product'}),
  (links:Page {name:'Links'}),
  (a:Page {name:'Site A'}),
  (b:Page {name:'Site B'}),
  (c:Page {name:'Site C'}),
  (d:Page {name:'Site D'}),

  (home)-[:LINKS {weight: 0.2}]->(about),
  (home)-[:LINKS {weight: 0.2}]->(links),
  (home)-[:LINKS {weight: 0.6}]->(product),
  (about)-[:LINKS {weight: 1.0}]->(home),
  (product)-[:LINKS {weight: 1.0}]->(home),
  (a)-[:LINKS {weight: 1.0}]->(home),
  (b)-[:LINKS {weight: 1.0}]->(home),
  (c)-[:LINKS {weight: 1.0}]->(home),
  (d)-[:LINKS {weight: 1.0}]->(home),
  (links)-[:LINKS {weight: 0.8}]->(home),
  (links)-[:LINKS {weight: 0.05}]->(a),
  (links)-[:LINKS {weight: 0.05}]->(b),
  (links)-[:LINKS {weight: 0.05}]->(c),
  (links)-[:LINKS {weight: 0.05}]->(d);

This is the way of using the pagerank algorithm in neo4j.

CALL gds.pageRank.stream('myGraph')
YIELD nodeId, score
RETURN gds.util.asNode(nodeId).name AS name, score
ORDER BY score DESC, name ASC

This result will appear on that query.

name        score
"Home"      3.215681999884452

"About"     1.0542700552146722

"Links"     1.0542700552146722

"Product"   1.0542700552146722
Waleed Ahmed Shahid
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6 Answers6

1

There is no built-in/library implementation of the algorithm in Apache AGE as you have demonstrated in Neo4j. I'd recommend writing a Python script instead to perform the PageRank algorithm. An example implementation is given as follows:

import age

def pagerank(graph, iterations=10):
  """
  Calculates the PageRank of all nodes in the graph.

  Args:
    graph: The graph to calculate PageRank on.
    iterations: The number of iterations to run PageRank for.

  Returns:
    A dictionary mapping node IDs to their PageRank scores.
  """

  pageranks = {}
  for node in graph.nodes:
    pageranks[node.id] = 1.0

  for _ in range(iterations):
    new_pageranks = {}
    for node in graph.nodes:
      score = 0.0
      for neighbor in graph.neighbors(node):
        score += pageranks[neighbor.id] / len(graph.neighbors(neighbor))
      new_pageranks[node.id] = score
    pageranks = new_pageranks

  return pageranks

if __name__ == "__main__":
  graph = age.open("mygraph.age")
  pageranks = pagerank(graph)
  for name, score in sorted(pageranks.items(), key=lambda x: x[1], reverse=True):
    print(f"{name}: {score}")

This code will first create a dictionary to store the PageRank scores of all nodes in the graph. Then, it will iterate for the specified number of iterations, updating the PageRank scores of each node based on the PageRank scores of its neighbors. Finally, it will print the PageRank scores of all nodes, sorted by their scores in descending order.

To run this code, you will need to install the age Python library. You can do this by running the following command:

pip install age

Once you have installed the age library, you can run the code by saving it as a Python file and then running it from the command line. For example, if you save the code as pagerank.py, you can run it by running the following command:

python pagerank.py

Please note, the code above is only a draft and I have not tested it myself. You may test and debug it.

This will print the PageRank scores of all nodes in the graph, sorted by their scores in descending order.

I hope this helps!

Abdullah Zia
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1

You can follow these steps to implement PageRank Algorithm in Apache AGE:

  • First you need to load your graph data into Apache AGE.
  • Then use g.pageRank() function to calculate the Pagerank of Pages(nodes).
  • After that you can get the results by selecting the id and page_rank() that are ordered by score in descending order.
adil shahid
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  • 4
1

Pagerank algorithm can be implemented in Apache AGE using the following approach:

  1. Setup the data by creating the necessary nodes and relationships in the graph.
  2. Next, initialise the pakerank algorithm by setting rank scores for each column in your table and apply iterative steps until convergence. This will give you a score based on the links and their associated weights.
  3. After running the pagerank algorithm, you can order the columns according to the rank and get the necessary insights.
Haseeb Ashraf
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0

I do not think a library implementation for PageRank currently exists for Apache AGE. You can look through Neo4j's implementation here for ideas on how to implement it in AGE using the Python driver.

tokamak32
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0
  • There is no built-in implementation of the PageRank algorithm in Apache AGE.
  • You need to write custom code or approach to tackle this problem.
Vinay-Agens
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  • 2
0

There is no built-in library implementation for PageRank Algorithm in Apache AGE.

Here are steps to implement Pagerank Algorithm in Apache AGE:

  • Create a table with two columns, one for the node and another for its neighbors.
  • Insert the nodes and their neighbor into the table.
  • Write a SQL query that calculates the PageRank score for each node based on its relationships with other nodes.
  • Use the WITH RECURSIVE clause to traverse the graph and calculate the PageRank score for each node recursively.
Pawan Kumar
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