  The exponential growth in scale and relevance of social networks enable them
to provide expansive insights. Predicting missing links in social networks
efficiently can help in various modern-day business applications ranging from
generating recommendations to influence analysis. Several categories of
solutions exist for the same. Here, we explore various feature extraction
techniques to generate representations of nodes and edges in a social network
that allow us to predict missing links. We compare the results of using ten
feature extraction techniques categorized across Structural embeddings,
Neighborhood-based embeddings, Graph Neural Networks, and Graph Heuristics,
followed by modeling with ensemble classifiers and custom Neural Networks.
Further, we propose combining heuristic-based features and learned
representations that demonstrate improved performance for the link prediction
task on social network datasets. Using this method to generate accurate
recommendations for many applications is a matter of further study that appears
very promising. The code for all the experiments has been made public.
