# Graph Structure¶

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In [1]:
# Setup Code toggle button
from IPython.core.display import HTML

HTML('''
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### Talk is cheap, show me the code.

In [2]:
# 22.01.2017
# Disable warnings because of nx.draw() output:
#     warnings.warn("axes.hold is deprecated, will be removed in 3.0")
import warnings
warnings.filterwarnings('ignore')

# Setup notebook theme
from jupyterthemes import get_themes
from jupyterthemes.stylefx import set_nb_theme
set_nb_theme(get_themes()[1])

Out[2]:

## Web Structure Metrics¶

Graph theoretic methods have been useful for searching, browsing and information gathering, and web mining. Using the terms from graph theory pages are referred to as nodes and links arcs. In graph theory some important terminology consists of:

• Directed Graph

• Set of nodes such that each arc is an ordered pair $(u,v)$ representing a connection $u\to v$.
• Out-Degree: number of arcs from $u$
• In-Degree: number of arcs to $u$
• If $\nexists \: u \to v$ then the degree is said to be $\infty$
• Strongly Connected Component (SCC): Set of nodes such that $\forall \: u,\,v \: \exists \: u \to v$
• There can be one or many SCCs
• Undirected Graph

• Set of nodes such that each arc is an unordered pair ${u,v}$
• Degree is equal to the number of edges
• Component: Set of nodes such that $\exists \: u \to v$

• Searches from starting node $u$ each layer of links
• Each layer $k$ consists of the nodes $k$ links from $u$, but not in an earlier layer
• Optimal, will always find the shortest path and it is the index of the layer
• If a node is not found in the search the distance is $\infty$
• Diameter

• Number of ordered pairs $(u,v)$ of the shortest path from $u$ to $v$.
• The average distance definition presents the problem of $\infty$
• The average distance or diameter is instead calculated from $P$
$$\text{let } P \text{ be the set of all ordered pairs } (u,v) \: \Big| \: u \to v$$

#### Macroscopic structure¶

About 90% of the nodes comprise a single highly connected component, and each of the four sets are approximately the same size.

1. (SCC) Strongly Connected Component: Pages reach each other by direct links.
2. IN: Pages that link into the SCC, but are not linked back to from the SCC.
3. OUT: Pages that are linked to by the SCC, but do not link back.
4. TENDRILS: Pages that can not reach the SCC, and cannot be reached from the SCC.

The power law:

$$\frac{1}{i^x} \quad \Big| \quad x > 1$$

The power law has also been observed to characterize user behavior in the web in accesses to web pages, numbers of times users access particular pages at a single site, and the in and out degrees of vertices on the web graph.

## A Sample Graph¶

The sample graph below is the dataset that will be used to demonstrate these principles. The heatmaping is based on the number of connections or degree.

In [3]:
import networkx as nx

# Build the graph
G = nx.Graph()
G.add_edges_from([('A', 'B'), ('B', 'C'), ('C', 'D'), ('C', 'A'), \
('C', 'G'), ('E', 'F'), ('G', 'C'), ('G', 'H'), \
('I', 'H'), ('I', 'K'), ('L', 'D'), ('M', 'A'), \
('M', 'N'), ('N', 'D'), ('O', 'A'), ('P', 'G')])

In [4]:
import pylab as plt

# with nodes colored by degree
node_color=[float(G.degree(v)) for v in G]
cmap = plt.cm.YlGnBu

plt.figure(figsize=(9,9), dpi=300)
pos = nx.nx_pydot.graphviz_layout(G,prog="neato")
nx.draw(G, pos, node_color=node_color, node_size=1000, edge_color='k', \
width=3, with_labels=True, cmap=plt.cm.coolwarm)

plt.show()


Some properties:

In [5]:
print("Number of nodes: {}".format(G.number_of_nodes()))
print("Number of edges: {}".format(G.number_of_edges()))

Number of nodes: 15
Number of edges: 15


The same data as a directed graph, here the heatmapping is based again on the degree of the node. From this graph it starts to become clearer what nodes form the various parts of the graph, SCC, IN, OUT, and TENDRILS.

In [6]:
H = nx.DiGraph()
H.add_edges_from([('A', 'B'), ('B', 'C'), ('C', 'D'), ('C', 'A'), \
('C', 'G'), ('E', 'F'), ('G', 'C'), ('G', 'H'), \
('I', 'H'), ('I', 'K'), ('L', 'D'), ('M', 'A'), \
('M', 'N'), ('N', 'D'), ('O', 'A'), ('P', 'G')])

In [7]:
import matplotlib.pyplot as plt
import pylab as plt

from networkx.drawing.nx_agraph import graphviz_layout

# with nodes colored by degree
node_color=[float(H.degree(v)) for v in H]

plt.figure(figsize=(9,9), dpi=300)
pos = nx.nx_pydot.graphviz_layout(H,prog="neato")
nx.draw(H, pos, node_color=node_color, node_size=1000, edge_color='k', \
width=3, with_labels=True, cmap=plt.cm.coolwarm)

plt.show()


Directed graph properties:

In [8]:
print("Number of nodes: {}".format(H.number_of_nodes()))
print("Number of edges: {}".format(H.number_of_edges()))
print("Strongly connected: {}".format(nx.is_strongly_connected(H)))
print("Number of strongly connected components: {}".format(nx.number_strongly_connected_components(H)))

Number of nodes: 15
Number of edges: 16
Strongly connected: False
Number of strongly connected components: 12


#### The SCC¶

Below we have the graph containing the SCC components, which consists of the nodes which are reachable from eachother. In this sample graph these nodes are A, B, C, and G.

In [9]:
# Build the SCC graph
for h in nx.strongly_connected_component_subgraphs(H):
if 'A' in h:
nx.draw(h, pos, node_color='#fb8072', node_size=1000, edge_color='k', \
width=3, with_labels=True)
else:
nx.draw(h, pos, node_color='#80b1d3', node_size=1000, edge_color='k', \
width=3, with_labels=True)

plt.show()


Going back to the directed graph:

In [10]:
SCC = ['A', 'B', 'C', 'G']
node_color = []
for node in H.nodes():
if node in SCC:
node_color.append('#fb8072')
else:
node_color.append('#80b1d3')

plt.figure(figsize=(9,9), dpi=300)
pos = nx.nx_pydot.graphviz_layout(H,prog="neato")
nx.draw(H, pos, node_color=node_color, node_size=1000, edge_color='k', \
width=3, with_labels=True)

plt.show()