This is a typical classification problem. I will explain how I would solve in a simplified way, PROVIDED THAT there is a temporal problem in classifying people that have already bought a ticket against people that have not yet bough a ticket (I.e you do not fully know whether such people have not bought but they will buy, or they haven’t bought and they will never buy). We will come back on this issue at the end of the answer. Anyway take this approach as a simplified and non-exhaustive example.
Firstly assume the whole dataset of the social network has N members, where m have actually bought the tickets, and N-m=k haven’t. Ignore the problem that some of them may do so in the near future before the event for now.
First you wish to collect more data about the users that in your opinion are suitable for a choice of buying the ticket (data that may be numerical or categorical about their spending capacity, proximity to the event, musical tastes, ... the more the better: I will leave this problem to your creativity; clearly the more the data collected are logically correlated to the purchase of a ticket the better it is, but also other data that are APPARENTLY not relevant may be relevant in the end, so just collect as many data as you can.. this is why data are so valuable nowadays!)
Then after doing this, you end up with a set of f features for all the N users. Now assume to split the k users not having bough yet in two groups of equal size. I am just saying equal size for convenience of explanation, maybe it is not even optimal. Suppose it is. Group 1, made of half of the individuals not having bought, is merged into the training dataset along with the m subjects. Group 2 is left outside.
Now your training sample is made of m+k/2 individuals. Now split it again and leave out a portion, suppose a 25% for a pure exemplification. This will be our validation dataset. Now define as training group the other (m+k/2)*75% subjects. And via machine learning (even via simple multivariate logistic regression here if the fit is good enough) you can solve a classification problem on the (m+k/2)*75% subject where the dependent variable is 1 if they bought a ticket and 0 if they didn’t. You will find the most important among the f features for each subject in predicting the choice and estimate an equation giving a score representing the probability that each subject buys a ticket based on his/her own features.
Now you can see what the predicting capability and classification power of the model is, by validating your model in the validation dataset of (m+k/2)*25% individuals. And compare different model hypotheses on that validation to see which of the model is better at classifying the subjects out of the initial training sample. Then you choose the model specification that does better (again, I am simplifying).
Now, after validation, you are ready to use your model chosen at previous step in order to predict the probability that, based on their f features, the people in Group 2 (do you remember the Group 2 defined at the beginning?) will buy the ticket in the near future. So you will have a classification model predicting the probability that the individuals out of the training and validation sample (that have not YET bought the tickets) either
So you will have an estimate of the probability that the subjects in Group 2 will belong to either class.
As anticipated there is a temporal problem in the observations. So if you do not need an estimate right now, I would prefer to wait up to the next event, to be sure that those people that have not bought a ticket now will never buy one for that event. And use the whole dataset available for this event to predict those people that, at the NEXT common event, will buy the ticket. In other words, the whole current dataset would be my dataset to build predictions for the NEXT event. That would be logically more precise as you will have a certain 1-0 dependent variable for people that have bought (1) and people that never bought (0), excluding those uncertain cases of people who haven’t bought yet, but they may immediately buy before the event (solving the temporal misalignment anticipated at the beginning of the answer).