Networks

Question 1

What forms the basis of friend recommendation systems?

Answer 1

open triangles: if “A” knows “B” and “A” knows “C”, then it’s probable that “B” also knows “C”

Question 2

How to get 2-item combinations from iterable?

Answer 2

itertools.combinations(iterable,2)

Question 3

What are Maximal cliques?

Answer 3

cliques that cannot be extended by adding an adjacent edge, and are a useful property of the graph when finding communities

Question 4

What is a clique?

Answer 4

A set of of nodes that are all connected to one another..

Question 5

How can a friend recommendation system be built using a graph of relationships?

Answer 5

For each node in the graph, check whether each pair of neighbours of the node have an edge between them. If they don’t, it’s an open triangle.

Question 6

How to analyze a subset of nodes in a network?

Answer 6

You can copy them out into another graph object using G.subgraph(nodes)

Question 7

How to draw a subgraph of G?

Answer 7

T_draw = G.subgraph(nodes_of_interest_and_neigbours)

nx. draw(T_draw)
plt. show()

Question 8

How to visualize the distribution of important nodes in graph?

Answer 8

plt. hist(list(nx.degree_centrality(G).values()))

plt. hist(list(nx.betweenness_centrality(G).values()))

Question 9

What is betweeness centrality?

Answer 9

Betweenness centrality is a node importance metric that uses information about the shortest paths in a network. It is defined as the fraction of all possible shortest paths between any pair of nodes that pass through the node.

Question 10

How to access metadata of nodes?

Answer 10

G.nodes(data=True)

Question 11

How to find maximal cliques in G?

Answer 11

nx.find_cliques(G)