Link Prediction for Social Networks using Representation Learning and Heuristic-based Features
This work addresses the problem of predicting missing links in social networks for applications like recommendations and influence analysis, but it is incremental as it builds on existing methods.
The paper tackles link prediction in social networks by comparing ten feature extraction techniques and proposing a combination of heuristic-based features and learned representations, which demonstrates improved performance on social network datasets.
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.