Demystifying LinkedIn using probabilities
Since last 3 years, I spend more than half an hour on LinkedIn everyday. This is what corporate life does to us. LinkedIn is a one stop solution for almost anything you want to know in this corporate environment. I use LinkedIn for reading interesting article, knowing interesting people, marketing etc. Here is a restriction LinkedIn puts on any non-premium users :
This article will give you a flavor of how you can use probability to think beyond what is visible to an average user in any social networking website. Here is simple tricks using probabilities on LinkedIn to overrule the restriction put by LinkedIn on its non premium users.
Sizing of 3rd/3rd+ level locked Linkedin friend :
Let’s start with estimating some numbers.
Total number of friends I have on my LinkedIn network = 1,700
Total number of 2nd degree friends on my LinkedIn network = 853,430
Total number of group users (share a group) = 973,323
The total of these numbers is the total number of profiles open to me to visit. I am likely to get profile restriction message for all other users. Let’s do a sizing of this number.
Say total number of users on LinkedIn = N
Number of friends of Mr.X = 10482
Number of common friends between Mr. X & Tavish Srivastava = 1
Probability of a person being a friend of Tavish = a = 1700/N
Probability of a person being a friend of Mr.X = b = 10482/N
Probability of a person being a friend of Mr.X & Tavish = c = 1/N
For independent events,
a * b = c
N = 1700 * 10482 / 1 = 17.8 Million
Number of profiles not open to me = 17.8 MM – 1.8 MM = 16 MM
2 steps to unlock details of 3rd/3rd+ level Linkedin friend :
We will like to open the full profile of this person without upgrading our account to premium. Let’s try to understand what factors linkedin takes into account before deciding whether a person X knows Y.
4 out of the 5 attributes of this list are very difficult to trigger unless until X actually knows Y. Last criterion is something one can easily manipulated. Here’s a simple way to do the same.
X reaches a Y’s profile but Y’s profile is locked. LinkedIn has a tab on left hand side called “people also viewed”. Consider following event :
P(A) : Random person views Y’s profile
P(B) : Random person views B’s profile.
B’s name comes in Y’s “people also viewed” tab 1st name. Hence, P(B/A) is extremely high.
We all know that P(B/A) ~ P(A/B) or both move in the same direction. If P(B/A) is high P(A/B) will also be high.
Hence, if X goes into B’s profile from this page, it is highly likely that X will be on B’s “people also viewed” tab. Also because X reaches B through Y’s profile and not a random search, he triggers the 5th criterion (among the 5 attributes identified above) and will be able to see complete B’s profile.
X finds Y on 4th number in the B’s “people also viewed” tab.
This time however X triggers 5th criterion for Y as well and hence linkedin shows X Y’s complete profile.
End Notes :
The algorithm works in most of the cases, however, in some cases you might not find the target in the list of “people also viewed” of the intermediate person. This is probably because you chose an intermediate person who might be much more popular than target person. In such cases try choosing people who have a closer relation to the target person. Please note that the article is based on my experience with analytics and frequent usage of LinkedIn, there is a possibility that we might have missed some of the variables LinkedIn takes into account. Do let us know of any attribute you think might be one of the variable LinkedIn takes into account and we missed to mention the same in this article.
Did you find the article useful? Share with us any other techniques which you know to make use of social networks in a better way. Also share with us any live examples of using probabilities in real world scenarios. Do let us know your thoughts about this article in the box below.