Link Prediction for Social Networks using Representation Learning and Heuristic-based Features

TL;DR

This paper investigates various feature extraction techniques, including structural, neighborhood, GNN, and heuristic methods, combined with ensemble and neural classifiers, to improve link prediction accuracy in social networks.

Contribution

It introduces a combined approach of heuristic-based features and learned representations, demonstrating enhanced link prediction performance on social network datasets.

Findings

Heuristic and learned features improve prediction accuracy.

Combining features outperforms individual methods.

Code for experiments is publicly available.

Abstract

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.