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Feb 18, 2024 126 likes •358 views

NETWORK ANALYSIS IN PYTHON II Definitions & basic recap Network Analysis in Python II Network/Graph Network = Graph = (nodes, edges) Directed or Undirected Facebook: Undirected Twi er: Directed networkx : API

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NETWORK ANALYSIS IN PYTHON II

Definitions & basic recap

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Network Analysis in Python II

Network/Graph

Network = Graph = (nodes, edges)
Directed or Undirected
Facebook: Undirected
Twier: Directed
networkx: API for analysis of graphs

node node edge

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Network Analysis in Python II

Basic NetworkX API

In [1]: import networkx as nx In [2]: G Out[2]: <networkx.classes.graph.Graph at 0x10b192da0> In [3]: G.nodes() Out[3]:['customer1', 'customer3', 'customer2'] In [4]: len(G.nodes()) Out[4]: 3 In [5]: len(G.edges()) Out[5]: 2 In [6]: type(G) Out[6]: networkx.classes.graph.Graph

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Network Analysis in Python II

Network visualization

nxviz: API for creating beautiful and rational graph viz
Prioritize placement of nodes

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Network Analysis in Python II

Basic nxviz API

In [7]: import nxviz as nv In [8]: import matplotlib.pyplot as plt In [9]: c = nv.CircosPlot(G) In[10]: c.draw() In[11]: plt.show()

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NETWORK ANALYSIS IN PYTHON II

Let’s practice!

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NETWORK ANALYSIS IN PYTHON II

Bipartite graphs

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Network Analysis in Python II

Bipartite graphs

A graph that is partitioned into two sets
Nodes are only connected to nodes in other partitions
Contrast: “unipartite”

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Network Analysis in Python II

Bipartite graphs: Example

customer1 customer2 customer3 product1 product2

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Network Analysis in Python II

Bipartite graphs in NetworkX

In [1]: import networkx as nx In [2]: G = nx.Graph() In [3]: numbers = range(3) In [4]: G.add_nodes_from(numbers, bipartite='customers') In [5]: letters = ['a', 'b'] In [6]: G.add_nodes_from(letters, bipartite='products')

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Network Analysis in Python II

Bipartite graphs in NetworkX

In [7]: G.nodes(data=True) Out[7]: [(0, {'bipartite': 'customers'}), (1, {'bipartite': 'customers'}), (2, {'bipartite': 'customers'}), ('b', {'bipartite': 'products'}), ('a', {'bipartite': 'products'})]

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Network Analysis in Python II

Degree centrality

Definition:
Number of possible neighbors depends on graph type

number of neighbors number of possible neighbors

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Network Analysis in Python II

Bipartite centrality metrics

Denominator: number of nodes in opposite partition, rather

than all other nodes

1 2 a b

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Network Analysis in Python II

Filtering graphs

In [1]: cust_nodes = [n for n, d in G.nodes(data=True) if ...: d['bipartite'] == 'customers'] In [2]: cust_nodes Out[2]: [(0, {'bipartite': 'customers'}), (1, {'bipartite': 'customers'}), (2, {'bipartite': 'customers'})] In [3]: nx.bipartite.degree_centrality(G, cust_nodes) Out[3]: {0: 0.5, 1: 0.5, 2: 1.0, 'a': 0.333, 'b': 1.0}

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NETWORK ANALYSIS IN PYTHON II

Let’s practice!

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NETWORK ANALYSIS IN PYTHON II

Bipartite graphs and recommendation systems

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Network Analysis in Python II

Recommendation systems

Previously: Recommended users to connect with one

another

Graph: "unipartite" (or users-only) version
Now: "bipartite" or (repo-users) version
Recommending repositories for users to work on

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Network Analysis in Python II

Recommendation systems

user1 user2 user3 repo1 repo2 repo3

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Network Analysis in Python II

Recommendation systems

user1 user2 user3 repo1 repo2 repo3

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Network Analysis in Python II

Recommendation systems

user1 user2 user3 repo1 repo2 repo3

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Network Analysis in Python II

Code: Node sets

In [1]: G.nodes(data=True) Out[1]: [('repo3', {'bipartite': 'repositories'}), ('repo1', {'bipartite': 'repositories'}), ('user1', {'bipartite': 'users'}), ('user2', {'bipartite': 'users'}), ('repo2', {'bipartite': 'repositories'}), ('user3', {'bipartite': 'users'})] In [2]: G.edges() Out[2]: [('repo1', 'user3'), ('user1', 'repo2'), ('user2', 'repo2'), ('repo2', ‘user3')]

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Network Analysis in Python II

Code: Node sets

In [3]: user1_nbrs = G.neighbors('user1') In [4]: user1_nbrs Out[4]: ['repo2'] In [5]: user3_nbrs = G.neighbors('user3') In [6]: user3_nbrs Out[6]: ['repo2', 'repo1'] In [7]: set(user1_nbrs).intersection(user3_nbrs) Out[7]: {'repo2'} In [8]: set(user3_nbrs).difference(user1_nbrs) Out[8]: {'repo1'}

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NETWORK ANALYSIS IN PYTHON II