Assume we have a group of 10 business engineering students working on a group project. Some of them follow data analytics (DA) and the others operations research (OR). They all belong to one of the two groups: DA or OR. These students sometimes collaborate when they are working on a group project. They can therefore be linked together to form a network. In this case, two students are connected if they are currently working together. As a result we have three types of edges in the network:
Dyadicity is the ratio between the actual and the expected number of same label edges and can be computed for each group.
bar_m_dd <- n_da*(n_da-1)/2*p
D_dd <- m_dd/bar_m_dd
D_dd
[1] 1.578947
bar_m_oo <- n_or*(n_or-1)/2*p
D_oo <- m_oo/bar_m_oo
D_oo
[1] 1.973684
Both groups are dyadic, i.e. there is more connectivity within the group of DA students and within the group of OR students. As a result, we can say that our network is homophilic in terms of their preferred partners for a group project.
Assume we have a group of 6 students: Tom, Sofie, Arno, Jan, Karen and Laura. Assume that Tom, Sofie and Arno are business engineering (B) students, while Jan, Karen and Laura are studying economics (E). They all belong to one of the two groups: B or E. All 6 students are going on an exchange programme next semester. Therefore, the students can be linked together based on the destination that they are going to:
| Destination | Student 1 | Student 2 |
|---|---|---|
| France | Tom | Arno |
| Switzerland | Sofie | Jan |
| China | Karen | Laura |
As a result we have three types of edges in the network:
Calculate the dyadicity for this network, and store it as D_bb and D_ee.
Assume that:
n_b and n_e, respectively.m_bb, m_ee, and m_be.p.