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NetworkX Tutorial Summary

This is a summary of NetworkX tutorial retrieved from the following website: Programminghistorian Tutorial

In [2]:
!pip3 install networkx
Collecting networkx
  Using cached networkx-2.5-py3-none-any.whl (1.6 MB)
Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.8/site-packages (from networkx) (4.4.2)
Installing collected packages: networkx
Successfully installed networkx-2.5
WARNING: You are using pip version 20.0.2; however, version 20.2.2 is available.
You should consider upgrading via the '/usr/local/bin/python -m pip install --upgrade pip' command.
In [4]:
ERROR: Could not find a version that satisfies the requirement pip3 (from versions: none)
ERROR: No matching distribution found for pip3
WARNING: You are using pip version 20.0.2; however, version 20.2.2 is available.
You should consider upgrading via the '/usr/local/bin/python -m pip install --upgrade pip' command.
In [32]:
Before beginning this tutorial, you will need to download two files that together constitute our network dataset. 
The file quakers_nodelist.csv is a list of early modern Quakers (nodes) 
the file quakers_edgelist.csv is a list of relationships between those Quakers (edges).

When you open the edge file, you will see that we use the names from the node file to identify the nodes connected by each edge. 
These edges begin at a source node and end at a target node. 
While this language derives from so-called directed network structures, we will be using our data as an undirected network: if Person A knows Person B, then Person B must also know Person A. 
In directed networks, relationships need not be reciprocal (Person A can send a letter to B without getting one back), but in undirected networks the connections are always reciprocal, or symmetric.
import csv 
from operator import itemgetter 
import networkx as nx 
from networkx.algorithms import community # this part of netwrkx, for community detection, needs to be imported separately 

with open('quakers_nodelist.csv', 'r') as nodecsv: # Open the file
    nodereader = csv.reader(nodecsv) # Read the csv
    # Retrieve the data (using Python list comprhension and list slicing to remove the header row, see footnote 3)
    nodes = [n for n in nodereader][1:]

node_names = [n[0] for n in nodes] # Get a list of only the node names

with open('quakers_edgelist.csv', 'r') as edgecsv: # Open the file
    edgereader = csv.reader(edgecsv) # Read the csv
    edges = [tuple(e) for e in edgereader][1:] # Retrieve the data
Format of data:
nodes as print - [['Joseph Wyeth', 'religious writer', 'male', '1663', '1731', '10013191'], ... ... ]
node_names = ['Joseph Wyeth', 'Alexander Skene of Newtyle', .... ]
edges as print - [('George Keith', 'Robert Barclay'), ... ... ]

print(len(node_names)) # supposedly 119 nodes 
print(len(edges))      # supposedly 174 edges 

G = nx.Graph()

G.add_nodes_from(node_names) # importing nodes 
G.add_edges_from(edges)      # importing edges 

This is one of several ways to add data to a network object. 
Check out (

Type: Graph
Number of nodes: 119
Number of edges: 174
Average degree:   2.9244
In [33]:
import matplotlib.pyplot as plt 
nx.draw(G, with_labels=True,font_size=5)
In [34]:
### Adding Attributes 

hist_sig_dict = {}
gender_dict = {}
birth_dict = {}
death_dict = {}
id_dict = {}

for node in nodes: # Loop through the list, one row at a time
    hist_sig_dict[node[0]] = node[1]
    gender_dict[node[0]] = node[2]
    birth_dict[node[0]] = node[3]
    death_dict[node[0]] = node[4]
    id_dict[node[0]] = node[5]

nx.set_node_attributes(G, hist_sig_dict, 'historical_significance')
nx.set_node_attributes(G, gender_dict, 'gender')
nx.set_node_attributes(G, birth_dict, 'birth_year')
nx.set_node_attributes(G, death_dict, 'death_year')
nx.set_node_attributes(G, id_dict, 'sdfb_id')

for n in G.nodes(): # Loop through every node, in our data "n" will be the name of the person
    print(n, G.nodes[n]['birth_year']) # Access every node by its name, and then by the attribute "birth_year"
Joseph Wyeth 1663
Alexander Skene of Newtyle 1621
James Logan 1674
Dorcas Erbery 1656
Lilias Skene 1626
William Mucklow 1630
Thomas Salthouse 1630
William Dewsbury 1621
John Audland 1630
Richard Claridge 1649
William Bradford 1663
Fettiplace Bellers 1687
John Bellers 1654
Isabel Yeamans 1637
George Fox the younger 1551
George Fox 1624
John Stubbs 1618
Anne Camm 1627
John Camm 1605
Thomas Camm 1640
Katharine Evans 1618
Lydia Lancaster 1683
Samuel Clarridge 1631
Thomas Lower 1633
Gervase Benson 1569
Stephen Crisp 1628
James Claypoole 1634
Thomas Holme 1626
John Freame 1665
John Swinton 1620
William Mead 1627
Henry Pickworth 1673
John Crook 1616
Gilbert Latey 1626
Ellis Hookes 1635
Joseph Besse 1683
James Nayler 1618
Elizabeth Hooten 1562
George Whitehead 1637
John Whitehead 1630
William Crouch 1628
Benjamin Furly 1636
Silvanus Bevan 1691
Robert Rich 1607
John Whiting 1656
Christopher Taylor 1614
Thomas Lawson 1630
Richard Farnworth 1630
William Coddington 1601
Thomas Taylor 1617
Richard Vickris 1590
Robert Barclay 1648
Jane Sowle 1631
Tace Sowle 1666
Leonard Fell 1624
Margaret Fell 1614
George Bishop 1558
Elizabeth Leavens 1555
Thomas Curtis 1602
Alice Curwen 1619
Alexander Parker 1628
John Wilkinson 1652
Thomas Aldam 1616
David Barclay of Ury 1610
David Barclay 1682
Sir Charles Wager 1666
George Keith 1638
James Parnel 1636
Peter Collinson 1694
Franciscus Mercurius van Helmont 1614
William Caton 1636
Francis Howgill 1618
Richard Hubberthorne 1628
William Ames 1552
William Rogers 1601
Isaac Norris 1671
Anthony Sharp 1643
Mary Fisher 1623
Anne Conway Viscountess Conway and Killultagh 1631
Samuel Fisher 1604
Francis Bugg 1640
Sarah Gibbons 1634
William Tomlinson 1650
Humphrey Norton 1655
William Gibson 1628
Gideon Wanton 1693
John Wanton 1672
Grace Chamber 1676
Mary Prince 1569
John Bartram 1699
Edward Haistwell 1658
John ap John 1625
John Rous 1585
Anthony Pearson 1627
Solomon Eccles 1617
John Burnyeat 1631
Edward Burrough 1633
Rebecca Travers 1609
William Edmundson 1627
Sarah Cheevers 1608
Edward Pyott 1560
Daniel Quare 1648
John Penington 1655
Mary Penington 1623
Charles Marshall 1637
Humphrey Woolrich 1633
William Penn 1644
Mary Pennyman 1630
Dorothy Waugh 1636
David Lloyd 1656
Lewis Morris 1671
Martha Simmonds 1624
John Story 1571
Thomas Story 1670
Thomas Ellwood 1639
William Simpson 1627
Samuel Bownas 1677
John Perrot 1555
Hannah Stranger 1656

Density of network

In [35]:
density = nx.density(G)
print("Network density:", density)
Network density: 0.02478279447372169
  • The output of density is a number, so that’s what you’ll see when you print the value.
  • In this case, the density of our network is approximately 0.0248. On a scale of 0 to 1, not a very dense network, which comports with what you can see in the visualization.
  • A 0 would mean that there are no connections at all, and a 1 would indicate that all possible edges are present (a perfectly connected network): this Quaker network is on the lower end of that scale, but still far from 0.

Shortest path measurement

  • A shortest path measurement is a bit more complex.
  • It calculates the shortest possible series of nodes and edges that stand between any two nodes, something hard to see in large network visualizations.
  • This measure is essentially finding friends-of-friends—if my mother knows someone that I don’t, then mom is the shortest path between me and that person.
  • The Six Degrees of Kevin Bacon game, from which our project takes its name, is basically a game of finding shortest paths (with a path length of six or less) from Kevin Bacon to any other actor.
In [36]:
fell_whitehead_path = nx.shortest_path(G, source="Margaret Fell", target="George Whitehead")

print("Shortest path between Fell and Whitehead:", fell_whitehead_path)
Shortest path between Fell and Whitehead: ['Margaret Fell', 'George Fox', 'George Whitehead']
In [37]:
print("Length of that path:", len(fell_whitehead_path)-1)
Length of that path: 2


  • There are many network metrics derived from shortest path lengths.
  • One such measure is diameter, which is the longest of all shortest paths.
  • After calculating all shortest paths between every possible pair of nodes in the network, diameter is the length of the path between the two nodes that are furthest apart.
  • The measure is designed to give you a sense of the network's overall size, the distance from one end of the network to another.
In [38]:
# If your Graph has more than one component, this will return False:

# Next, use nx.connected_components to get the list of components,
# then use the max() command to find the largest one:
components = nx.connected_components(G)
largest_component = max(components, key=len)

# Create a "subgraph" of just the largest component
# Then calculate the diameter of the subgraph, just like you did with density.

subgraph = G.subgraph(largest_component)
diameter = nx.diameter(subgraph)
print("Network diameter of largest component:", diameter)
Network diameter of largest component: 8

Triadic closure

Unlike density which is scaled from 0 to 1, it is difficult to know from this number alone whether 8 is a large or small diameter. For some global metrics, it can be best to compare it to networks of similar size and shape.

  • Triadic closure supposes that if two people know the same person, they are likely to know each other.
  • If Fox knows both Fell and Whitehead, then Fell and Whitehead may very well know each other, completing a triangle in the visualization of three edges connecting Fox, Fell, and Whitehead.
  • The number of these enclosed triangles in the network can be used to find clusters and communities of individuals that all know each other fairly well.
  • One way of measuring triadic closure is called clustering coefficient because of this clustering tendency, but the structural network measure you will learn is known as transitivity.
  • Transitivity is the ratio of all triangles over all possible triangles.
  • A possible triangle exists when one person (Fox) knows two people (Fell and Whitehead).
  • So transitivity, like density, expresses how interconnected a graph is in terms of a ratio of actual over possible connections.
  • Remember, measurements like transitivity and density concern likelihoods rather than certainties.
  • All the outputs of your Python script must be interpreted, like any other object of research.
  • Transitivity allows you a way of thinking about all the relationships in your graph that may exist but currently do not.
In [39]:
triadic_closure = nx.transitivity(G)
print("Triadic closure:", triadic_closure)
Triadic closure: 0.16937799043062202

Also like density, transitivity is scaled from 0 to 1, and you can see that the network’s transitivity is about 0.1694, somewhat higher than its 0.0248 density.

Because the graph is not very dense, there are fewer possible triangles to begin with, which may result in slightly higher transitivity.

That is, nodes that already have lots of connections are likely to be part of these enclosed triangles. To back this up, you’ll want to know more about nodes with many connections.


  • After getting some basic measures of the entire network structure, a good next step is to find which nodes are the most important ones in your network.
  • In network analysis, measures of the importance of nodes are referred to as centrality measures.
  • Because there are many ways of approaching the question "which nodes are the most important?" there are many different ways of calculating centrality.
  • Here you will learn about three of the most common centrality measures: degree, betweenness centrality, and eigenvector centrality.


Degree is the simplest and the most common way of finding important nodes.

  • a node's degree is the sum of its edges.
  • if a node has three lines extending from it to other nodes, its degree is three.
  • Five edges, its degree is five.
  • The nodes with the highest degree in a social network are the people who know the most people.
  • These nodes are often referred to as hubs, and calculating degree is the quickest way of identifying hubs.
In [40]:
print(G.nodes['William Penn'])
{'historical_significance': 'Quaker leader and founder of Pennsylvania', 'gender': 'male', 'birth_year': '1644', 'death_year': '1718', 'sdfb_id': '10009531'}
In [41]:
degree_dict = dict(
nx.set_node_attributes(G, degree_dict, 'degree')
print(G.nodes['William Penn'])
{'historical_significance': 'Quaker leader and founder of Pennsylvania', 'gender': 'male', 'birth_year': '1644', 'death_year': '1718', 'sdfb_id': '10009531', 'degree': 18}
In [42]:
sorted_degree = sorted(degree_dict.items(), key=itemgetter(1), reverse=True)
print("Top 20 nodes by degree:")
for d in sorted_degree[:20]:
Top 20 nodes by degree:
('George Fox', 22)
('William Penn', 18)
('James Nayler', 16)
('George Whitehead', 13)
('Margaret Fell', 13)
('Benjamin Furly', 10)
('Edward Burrough', 9)
('George Keith', 8)
('Thomas Ellwood', 8)
('Francis Howgill', 7)
('John Perrot', 7)
('John Audland', 6)
('Richard Farnworth', 6)
('Alexander Parker', 6)
('John Story', 6)
('John Stubbs', 5)
('Thomas Curtis', 5)
('John Wilkinson', 5)
('William Caton', 5)
('Anthony Pearson', 5)
In [43]:
betweenness_dict = nx.betweenness_centrality(G) # Run betweenness centrality
eigenvector_dict = nx.eigenvector_centrality(G) # Run eigenvector centrality

# Assign each to an attribute in your network
nx.set_node_attributes(G, betweenness_dict, 'betweenness')
nx.set_node_attributes(G, eigenvector_dict, 'eigenvector')
In [44]:
sorted_betweenness = sorted(betweenness_dict.items(), key=itemgetter(1), reverse=True)

print("Top 20 nodes by betweenness centrality:")
for b in sorted_betweenness[:20]:
Top 20 nodes by betweenness centrality:
('William Penn', 0.23999456006192205)
('George Fox', 0.23683257726065216)
('George Whitehead', 0.12632024847366005)
('Margaret Fell', 0.12106792237170329)
('James Nayler', 0.10446026280446098)
('Benjamin Furly', 0.06419626175167242)
('Thomas Ellwood', 0.046190623885104545)
('George Keith', 0.045006564009171565)
('John Audland', 0.04164936340077581)
('Alexander Parker', 0.03893676140525336)
('John Story', 0.028990098622866983)
('John Burnyeat', 0.028974117533439564)
('John Perrot', 0.02829566854990583)
('James Logan', 0.026944806605823553)
('Richard Claridge', 0.026944806605823553)
('Robert Barclay', 0.026944806605823553)
('Elizabeth Leavens', 0.026944806605823553)
('Thomas Curtis', 0.026729751729751724)
('John Stubbs', 0.024316593960227152)
('Mary Penington', 0.02420824624214454)
  • As you can see, Penn’s degree is 18, relatively high for this network.
  • But printing out this ranking information illustrates the limitations of degree as a centrality measure.
  • You probably didn’t need NetworkX to tell you that William Penn, Quaker leader and founder of Pennsylvania, was important.
  • Most social networks will have just a few hubs of very high degree, with the rest of similar, much lower degree.
  • 12 Degree can tell you about the biggest hubs, but it can’t tell you that much about the rest of the nodes.
  • And in many cases, those hubs it’s telling you about (like Penn or Quakerism co-founder Margaret Fell, with a degree of 13) are not especially surprising.
  • In this case almost all of the hubs are founders of the religion or otherwise important political figures.

Eigenvector centrality and Betweenness centrality

Eigenvector Centrality

  • it looks at a combination of a node’s edges and the edges of that node’s neighbors.
  • Eigenvector centrality cares if you are a hub, but it also cares how many hubs you are connected to.
  • It’s calculated as a value from 0 to 1: the closer to one, the greater the centrality.
  • Eigenvector centrality is useful for understanding which nodes can get information to many other nodes quickly.
  • If you know a lot of well-connected people, you could spread a message very efficiently.
  • If you’ve used Google, then you’re already somewhat familiar with Eigenvector centrality.
  • Their PageRank algorithm uses an extension of this formula to decide which webpages get to the top of its search results.

Betweenness Centrality

  • Betweenness centrality is a bit different from the other two measures in that it doesn’t care about the number of edges any one node or set of nodes has.
  • Betweenness centrality looks at all the shortest paths that pass through a particular node (see above).
  • To do this, it must first calculate every possible shortest path in your network, so keep in mind that betweenness centrality will take longer to calculate than other centrality measures (but it won’t be an issue in a dataset of this size).
  • Betweenness centrality, which is also expressed on a scale of 0 to 1, is fairly good at finding nodes that connect two otherwise disparate parts of a network.
  • If you’re the only thing connecting two clusters, every communication between those clusters has to pass through you.
  • In contrast to a hub, this sort of node is often referred to as a broker.
  • Betweenness centrality is not the only way of finding brokerage (and other methods are more systematic), but it’s a quick way of giving you a sense of which nodes are important not because they have lots of connections themselves but because they stand between groups, giving the network connectivity and cohesion.
In [45]:
#First get the top 20 nodes by betweenness as a list
top_betweenness = sorted_betweenness[:20]

#Then find and print their degree
for tb in top_betweenness: # Loop through top_betweenness
    degree = degree_dict[tb[0]] # Use degree_dict to access a node's degree, see footnote 2
    print("Name:", tb[0], "| Betweenness Centrality:", tb[1], "| Degree:", degree)
Name: William Penn | Betweenness Centrality: 0.23999456006192205 | Degree: 18
Name: George Fox | Betweenness Centrality: 0.23683257726065216 | Degree: 22
Name: George Whitehead | Betweenness Centrality: 0.12632024847366005 | Degree: 13
Name: Margaret Fell | Betweenness Centrality: 0.12106792237170329 | Degree: 13
Name: James Nayler | Betweenness Centrality: 0.10446026280446098 | Degree: 16
Name: Benjamin Furly | Betweenness Centrality: 0.06419626175167242 | Degree: 10
Name: Thomas Ellwood | Betweenness Centrality: 0.046190623885104545 | Degree: 8
Name: George Keith | Betweenness Centrality: 0.045006564009171565 | Degree: 8
Name: John Audland | Betweenness Centrality: 0.04164936340077581 | Degree: 6
Name: Alexander Parker | Betweenness Centrality: 0.03893676140525336 | Degree: 6
Name: John Story | Betweenness Centrality: 0.028990098622866983 | Degree: 6
Name: John Burnyeat | Betweenness Centrality: 0.028974117533439564 | Degree: 4
Name: John Perrot | Betweenness Centrality: 0.02829566854990583 | Degree: 7
Name: James Logan | Betweenness Centrality: 0.026944806605823553 | Degree: 4
Name: Richard Claridge | Betweenness Centrality: 0.026944806605823553 | Degree: 2
Name: Robert Barclay | Betweenness Centrality: 0.026944806605823553 | Degree: 3
Name: Elizabeth Leavens | Betweenness Centrality: 0.026944806605823553 | Degree: 2
Name: Thomas Curtis | Betweenness Centrality: 0.026729751729751724 | Degree: 5
Name: John Stubbs | Betweenness Centrality: 0.024316593960227152 | Degree: 5
Name: Mary Penington | Betweenness Centrality: 0.02420824624214454 | Degree: 4

Advanced NetworkX: Community detection with modulairty

  • Another common thing to ask about a network dataset is what the subgroups or communities are within the larger social structure.
  • Is your network one big, happy family where everyone knows everyone else? Or is it a collection of smaller subgroups that are only connected by one or two intermediaries?
  • The field of community detection in networks is designed to answer these questions.
  • There are many ways of calculating communities, cliques, and clusters in your network, but the most popular method currently is modularity.
  • Modularity is a measure of relative density in your network: a community (called a module or modularity class) has high density relative to other nodes within its module but low density with those outside.
  • Modularity gives you an overall score of how fractious your network is, and that score can be used to partition the network and return the individual communities.
  • Very dense networks are often more difficult to split into sensible partitions.
  • Luckily, as you discovered earlier, this network is not all that dense.
  • There aren’t nearly as many actual connections as possible connections, and there are several altogether disconnected components.
  • Its worthwhile partitioning this sparse network with modularity and seeing if the result make historical and analytical sense.
  • Community detection and partitioning in NetworkX requires a little more setup than some of the other metrics.
  • There are some built-in approaches to community detection (like minimum cut, but modularity is not included with NetworkX.
  • Fortunately there’s an additional python module you can use with NetworkX, which you already installed and imported at the beginning of this tutorial.
  • You can read the full documentation for all of the functions it offers, but for most community detection purposes you’ll only want best_partition():
In [46]:
communities = community.greedy_modularity_communities(G) #tries to determine the number of communities appropriate for the graph, and groups all nodes into subsets based on these communities.

the above code will not create a dictionary. Instead it creates a list of special “frozenset” objects (similar to lists).
There’s one set for each group, and the sets contain the names of the people in each group. 
In order to add this information to your network in the now-familiar way, you must first create a dictionary that labels each person with a number value for the group to which they belong:

modularity_dict = {} # Create a blank dictionary
for i,c in enumerate(communities): # Loop through the list of communities, keeping track of the number for the community
    for name in c: # Loop through each person in a community
        modularity_dict[name] = i # Create an entry in the dictionary for the person, where the value is which group they belong to.

# Now you can add modularity information like we did the other metrics
nx.set_node_attributes(G, modularity_dict, 'modularity')

# First get a list of just the nodes in that class
class0 = [n for n in G.nodes() if G.nodes[n]['modularity'] == 0]

# Then create a dictionary of the eigenvector centralities of those nodes
class0_eigenvector = {n:G.nodes[n]['eigenvector'] for n in class0}

# Then sort that dictionary and print the first 5 results
class0_sorted_by_eigenvector = sorted(class0_eigenvector.items(), key=itemgetter(1), reverse=True)

print("Modularity Class 0 Sorted by Eigenvector Centrality:")
for node in class0_sorted_by_eigenvector[:5]:
    print("Name:", node[0], "| Eigenvector Centrality:", node[1])
Modularity Class 0 Sorted by Eigenvector Centrality:
Name: William Penn | Eigenvector Centrality: 0.2703220115399868
Name: George Keith | Eigenvector Centrality: 0.18384690867915351
Name: William Bradford | Eigenvector Centrality: 0.06812170326615953
Name: Tace Sowle | Eigenvector Centrality: 0.04688085927497436
Name: James Logan | Eigenvector Centrality: 0.044474460267486554
  • Using eigenvector centrality as a ranking can give you a sense of the important people within this modularity class.
  • You’ll notice that some of these people, especially William Penn, William Bradford (not the Plymouth founder you’re thinking of), and James Logan, spent lots of time in America.
  • Also, Bradford and Tace Sowle were both prominent Quaker printers. With just a little bit of digging, we can discover that there are both geographical and occupational reasons that this group of people belongs together.
  • This is an indication that modularity is probably working as expected.
  • In smaller networks like this one, a common task is to find and list all of the modularity classes and their members.
  • You can do this by looping through the communities list:
In [47]:
for i,c in enumerate(communities): # Loop through the list of communities
    if len(c) > 2: # Filter out modularity classes with 2 or fewer nodes
        print('Class '+str(i)+':', list(c)) # Print out the classes and their members
Class 0: ['David Lloyd', 'Samuel Bownas', 'James Logan', 'Joseph Besse', 'Peter Collinson', 'Richard Claridge', 'William Penn', 'Isaac Norris', 'Thomas Story', 'John Bartram', 'Tace Sowle', 'Isabel Yeamans', 'Edward Haistwell', 'George Keith', 'Anne Conway Viscountess Conway and Killultagh', 'William Bradford', 'Jane Sowle']
Class 1: ['Gervase Benson', 'William Gibson', 'Martha Simmonds', 'James Nayler', 'Robert Rich', 'Thomas Lower', 'Dorcas Erbery', 'Elizabeth Leavens', 'Anthony Pearson', 'Richard Farnworth', 'Margaret Fell', 'Thomas Holme', 'William Tomlinson', 'Thomas Aldam', 'Francis Howgill', 'George Fox the younger', 'Hannah Stranger']
Class 2: ['Ellis Hookes', 'William Mucklow', 'William Mead', 'Mary Fisher', 'John Crook', 'William Crouch', 'Thomas Salthouse', 'Elizabeth Hooten', 'John Perrot', 'Leonard Fell', 'Mary Prince', 'Edward Burrough', 'William Dewsbury', 'George Fox', 'William Coddington']
Class 3: ['Francis Bugg', 'Alice Curwen', 'Daniel Quare', 'George Whitehead', 'Rebecca Travers', 'Lewis Morris', 'Sir Charles Wager', 'Thomas Lawson', 'Richard Hubberthorne', 'John Whitehead', 'Alexander Parker', 'Silvanus Bevan', 'Gilbert Latey', 'Henry Pickworth']
Class 4: ['John Penington', 'William Edmundson', 'Thomas Ellwood', 'John ap John', 'Anthony Sharp', 'Mary Penington', 'Thomas Curtis', 'Joseph Wyeth', 'James Claypoole', 'John Burnyeat', 'William Simpson', 'Samuel Clarridge', 'William Rogers']
Class 5: ['Samuel Fisher', 'Benjamin Furly', 'Robert Barclay', 'Stephen Crisp', 'John Stubbs', 'William Ames', 'David Barclay of Ury', 'William Caton', 'James Parnel', 'John Swinton', 'Franciscus Mercurius van Helmont']
Class 6: ['Thomas Camm', 'John Audland', 'John Camm', 'Charles Marshall', 'John Wilkinson', 'Edward Pyott', 'Solomon Eccles', 'John Story', 'Anne Camm']
Class 7: ['Christopher Taylor', 'John Whiting', 'Thomas Taylor']

Exporting Data

gexf format is readable format for Gephi

In [48]:
nx.write_gexf(G, 'quaker_network.gexf')
In [ ]:
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