# -*- coding: utf-8 -*-
from graphgenpy import GraphGenerator
from graphgenpy import utils
import os
import networkx as nx
# Give me a graph of actors (role=1) that have played in the same movie, only for movie_ids 0-200
datalogQuery = """
Nodes(id,name):- name(id,name),cast_info(_,id,movie_id,_,_,_,role),movie_id <=200,role='1'.
Edges(id1,id2):- cast_info(_,id1,movie_id,_,_,_,role),cast_info(_,id2,movie_id,_,_,_,role), role='1',movie_id<= 200.
"""
filename = 'coactorship'
# Specify database connection details and instanciate GraphGen object
gg = GraphGenerator("imdb","localhost","5432","kostasx","pass")
# Evaluate graph extraction query and serialize the resulting graph to disk in a standard format
fname = gg.generateGraph(datalogQuery,filename,GraphGenerator.GML)
# Import graph into NetworkX by reading the serialized graph
#for GML Format
G = nx.read_gml(fname,'id');
print "Graph Loaded into NetworkX! Running PageRank..."
nx.pagerank(G)
print "Done!"
from graphgenpy import GraphGenerator
import networkx as nx
# a graph of authors if they've bought the same part.
datalogQuery = """
Nodes(ID, Name) :- Customer(ID, Name).
Edges(ID1, ID2) :- Orders(orderId1, ID1),Lineitem(orderId1,part),Orders(orderId2, ID2),Lineitem(orderId2,part),part < 1000.
"""
# Credentials for connecting to the database
gg = GraphGenerator("tpch", "localhost", "5432", "kostasx",
"password") #All these must be strings!!
# Evaluate graph extraction query and serialize the resulting graph to disk in a standard format. Return the file's name in the FS.
extracted_name = "extracted_graph_tpch"
fname = gg.generateGraph(datalogQuery, extracted_name, GraphGenerator.GML)
# Load graph into NetworkX
G = nx.read_gml(fname, 'id') #by default, the graph format will me gml
print "Graph Loaded into NetworkX! Running PageRank..."
# Run any algorithm on the graph using NetowrkX
# print nx.pagerank(G)
print "Done!"
from graphgenpy import GraphGenerator
import networkx as nx
# a graph of authors if they've bought the same part.
datalogQuery = """
Nodes(ID, Name) :- Customer(ID, Name).
Edges(ID1, ID2) :- Orders(orderId1, ID1),Lineitem(orderId1,part),Orders(orderId2, ID2),Lineitem(orderId2,part),part < 1000.
"""
# Credentials for connecting to the database
gg = GraphGenerator("tpch","localhost","5432","kostasx","password") #All these must be strings!!
# Evaluate graph extraction query and serialize the resulting graph to disk in a standard format. Return the file's name in the FS.
extracted_name = "extracted_graph_tpch"
fname = gg.generateGraph(datalogQuery,extracted_name,GraphGenerator.GML)
# Load graph into NetworkX
G = nx.read_gml(fname,'id') #by default, the graph format will me gml
print "Graph Loaded into NetworkX! Running PageRank..."
# Run any algorithm on the graph using NetowrkX
# print nx.pagerank(G)
print "Done!"
from graphgenpy import GraphGenerator
import networkx as nx
# a graph of authors if they've bought the same part.
datalogQuery = """
Nodes(ID, Name) :- Customer(ID, Name).
Edges(ID1, ID2) :- Orders(orderId1, ID1),Lineitem(orderId1,part),Orders(orderId2, ID2),Lineitem(orderId2,part),part < 1000.
"""
# Credentials for connecting to the database
gg = GraphGenerator("localhost","5432","tpch","kostasx","password") #All these must be strings!!
# Evaluate graph extraction query and serialize the resulting graph to disk in a standard format. Return the file's name in the FS.
fname = gg.generateGraph(datalogQuery,"extracted_graph_tpch",GraphGenerator.GML)
# Load graph into NetworkX
G = nx.read_gml(fname,'id') #by default, the graph format will me gml
print "Graph Loaded into NetworkX! Running PageRank..."
# Run any algorithm on the graph using NetowrkX
# print nx.pagerank(G)
print "Done!"
