Identifying triangle relationships

Now that you've learned about cliques, it's time to try leveraging what you know to find structures in a network. Triangles are what you'll go for first. We may be interested in triangles because they're the simplest complex clique. Let's write a few functions; these exercises will bring you through the fundamental logic behind network algorithms.

In the Twitter network, each node has an 'occupation' label associated with it, in which the Twitter user's work occupation is divided into celebrity, politician and scientist. One potential application of triangle-finding algorithms is to find out whether users that have similar occupations are more likely to be in a clique with one another.

Import combinations from itertools.
Write a function is_in_triangle() that has two parameters - G and n - and checks whether a given node is in a triangle relationship or not.
- combinations(iterable, n) returns combinations of size n from iterable. This will be useful here, as you want combinations of size 2 from G.neighbors(n).
- To check whether an edge exists between two nodes, use the .has_edge(node1, node2) method. If an edge exists, then the given node is in a triangle relationship, and you should return True.

Exercise

Identifying triangle relationships

Instructions