I am a 17-year-old student (Year 12 / 11th grade) writing an essay on link prediction algorithms and am new to neo4j so bear with me as I attempt to abysmally unfold my thought process in this question.
I am attempting to create a data set of a group of people with different relationships such as friends and co-workers etc. and then use the Adamic-Agar algorithm and preferential treatment algorithm available through the Graph Data Science plugin to test their prediction accuracy. I was wondering if it impossible to give the relationships weighting so that a friend is more important than a co-worker when the algorithm runs.
Also please inform me if there would be a better way for me to go about performing this experiment whether it would be using a different program or changing how I am thinking about the experiment.
Thanks for any help,
For my experimentation I will take an Instagram account with roughly 50 followers and use them to create my data set (weighting mutual friends as 1 and non-mutual friends as 0.5).
I will leave some of the links undone to test they accuracy of the algorithm to see if the algorithm will fill in the blanks and use this to create a confusion matrix. However on the Graph Data Science Plugin the algorithm is shown to only be able to predict the chance of one link at a time.
Therefore my question is will I have to predict the chance of each link individually or can I have the algorithm predict all of them and rank them in terms of their score? If so how will I go about doing this?