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.

翻译：社交网络在规模与相关性上的指数级增长，使其能够提供丰富的洞察。高效预测社交网络中的缺失链接有助于各类现代商业应用，从推荐生成到影响力分析。现有多种解决方案可应对该问题。本文探索了多种特征提取技术，用于生成社交网络中节点与边的表示，进而实现缺失链接的预测。我们比较了十种特征提取方法的结果，这些方法涵盖结构嵌入、邻域嵌入、图神经网络以及图启发式方法，随后采用集成分类器与定制神经网络进行建模。进一步地，我们提出将启发式特征与学习到的表示相结合，该方法在社交网络数据集上的链接预测任务中展现出更优性能。利用该方法为众多应用生成准确推荐，是一项前景广阔的后续研究方向。所有实验代码均已公开。