Blockchain-Enabled Trustworthy Friend Recommendation via Structural Similarity in Ego Networks

As mobile apps and online interaction patterns develop, online social networks have become an essential tool for expanding social circles. Stranger socializing is a key way to broaden these circles. However, stranger socializing in online social networks faces challenges like privacy leaks, lack of social trust, and issues with personalized recommendations. This study combines blockchain with stranger social networks. It implements an ego network based approximate graph edit distance algorithm via smart contracts on the blockchain social network. The algorithm clusters ego networks to simulate users’ social circles. Then, it uses the graph’s topology (node degree and edges) to approximate graph edit distances, measuring node similarity for stranger friend recommendations. Finally, experiments on multiple real world social network datasets validate the study’s effectiveness.