I was planning to use a graph theory / page-rank approach to find the most influential person in an organization.
Influential person is someone who drives a lot of activity in the organization. When he assigns a task, most people do it. When he sends a mail, most people reply to him. When he assigns some training, most people do it.
But there is one drawback to using graph theory here. For example, say Person A is very influential, and Person B has just joined an organization and works under A. B puts in a leave request and sends it to A for approval. A approves the leave request. According to graph theory, B is very influential because A responded to B. But this is not correct. This approach would give very bad results.
How can i overcome this limitation? Can anyone suggest an alternative approach?
I am using number of interactions to calculate an affinity scores between two users. Based on this, I suggest what content should be shown to a user (like FaceBook does). User B responds to User A most of the time. so (B->A) has a high affinity score. So, in user B's newsfeed I intend to show content coming from User A.
Obviously different type of interactions will have different weights. But is there a better way than just counting number of interactions?
If two users A & C have same affinity scores with B, if A is more influential than B, A's content will be shown first to user A.