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Neo4j Tutorial

Fundamentals

Store any kind of data using the following graph concepts:

• Node: Graph data records
• Relationship: Connect nodes (has direction and a type)
• Property: Stores data in key-value pair in nodes and relationships
• Label: Groups nodes and relationships (optional)

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# https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/
import requests
from selectolax.parser import HTMLParser

base_url = 'https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/'

r = requests.get(base_url)
tree = HTMLParser(r.content)

for node in tree.css('a'):

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import functools

#from numba import jit
import numpy as np
from scipy import sparse


def random_walks(adj_list, r, l, p=1, q=1, verbose=False):
    """
    Biased random walk starting from node i.

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def find_all_shortest_paths(dic_node, n1, n2, step_threshold):
    return_paths = []
    visit_queue = [[n1]]
    # this can be updated to get all shortest paths for all visited nodes
    # for the shortest path from N1 to N2, all intermediate paths are also shortest paths between their respective nodes.
    visited_nodes_prev = set()
    visited_nodes = set()
    cur_distance = 0
    while len(visit_queue):
        cur_path = visit_queue.pop(0)

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# Using a sparse matrix imported from...

import numpy as np
from scipy import sparse, io


def pagerank(adjacency, probs=None, n_iters=20, resid=0.85, modify_matrix=True):
    """
    Args:
        adjacency - sparse matrix

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import subprocess

import numpy as np
from scipy import sparse, io

threshold = 1000
folders = ['M7_5', 'M8_3', 'X5.3.4', 'X5_2', 'X6.1']

for f in folders:
    print(f)

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#!/usr/bin/env python3

import sys
import textstat

path = 'scripted_test_outputs/000_output.txt'

if len(sys.argv) > 1:
    path = sys.argv[1]

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import os
import subprocess

filenames = os.listdir('.')
for filename in filenames:
    name = filename.split('_')[0]
    if not os.path.exists(name):
        os.makedirs(name)
    os.rename(filename, os.path.join(name, filename))
    if filename.endswith('zip'):

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# makes an svg bingo board
import svgwrite

def text_to_font_size(text):
    if isinstance(text, str):
        total_text = text
    else:
        total_text = ''.join(text)
    if len(total_text) < 10:
        return 18

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import numpy as np
from sklearn.cluster.bicluster import SpectralCoclustering

spec = SpectralCoclustering(18)
cluster_counts_subset = np.vstack([cluster_counts[:31, :], cluster_counts[32:,:]])
spec.fit(cluster_counts + 0.0001)
row_labels = spec.row_labels_
column_labels = spec.column_labels_

row_order = np.argsort(row_labels)