Social Network Recommendations for Friends with Neo4j Graph Database

Ứng dụng cơ sở dữ liệu đồ thị Neo4j xây dựng hệ thống khuyến nghị kết bạn trên mạng xã hội

  • Thuy Pham Thi Thu Nha Trang University
  • Thanh Nguyen Thi Thai
  • Hwa Soo Kim
Keywords: Social Network, Graph Database, Neo4j, Recommendation, Truth algorithm


In recent years, along with the development of the internet, the number of social network users is increasing day by day. Through social networks, users can share, exchange information or make friends with each other. However, with new relationships, users often have a need to assess the credibility of a new friend before making friends on social networks. This paper proposes a friend recommendation system on social networks based on how to calculate the reliability between users. The recommendation system is implemented using the Neo4j graph database. The results of the truth algorithm proposed in this paper are higher than other similar algorithms.


Neo4j, “Neo4j Documentation”,, accessed date: March 10th, 2022., accessed date March 10th, 2022., accessed date March 10th, 2023., accessed date March 10th, 2023.

Cypher Query Language,, accessed date March 10th, 2023.

Iftikhar Ahmad, Muhammad Usman Akhtar, Salma Noor & Ambreen Shahnaz, Missing Link Prediction using Common Neighbor and Centrality based Parameterized Algorithm, Sci Rep 10, 364 (2020). 57304-y

Sourabh Dadapure, Recommendation Engine using Adamic Adar Measure, American Society for Engineering Education, 2022.

Sheridan, P., Onodera, T., A Preferential Attachment Paradox: How Preferential Attachment Combines with Growth to Produce Networks with Log-normal In-degree Distributions, Sci Rep 8, 2811 (2018). 21133-2.

Pham Thi Thu Thuy, Algorithm to determine the trustworthiness of friends on social networks based on Ontology, Science Journal of Da Lat University Vol. 8, Issue 2, 2018 139–150.

Graph API,, March 10th, 2023.

Ling Chen, Man Gao, Bin Li, Wei Liu, Bolun Chen, Detect potential relations by link prediction in multi-relational social networks, Decsup (2018), doi:10.1016/j.dss.2018.09.006.

Kai Zhu, Meng Cao, Heng-yang Lu, MALP: A More Effective Meta-Paths Based Link Prediction Method in PartiallyAligned Heterogeneous Social Networks, 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI).

Ling Chen, Man Gao, Bin Li, Wei Liu, Bolun Chen, Detect Potential Relations by Link Prediction in MultiRelational Social Networks, Decsup (2018), doi: 10.1016/j.dss.2018.09.006.

Pinghua Xu, Wenbin Hu, Jia Wu, Bo Du, Link Prediction with Signed Latent Factors in Signed Social Networks, Conference on Knowledge Discovery and Data Mining (KDD ’19), August 4–8, 2019, Anchorage, AK, USA. ACM, New York, NY, USA, 9 pages.

Virinchi Srinivas, Pabitra Mitra, Link Prediction in Social Networks, Springer, 2016.

Jan Skrasek, Social Network Recommendation using Graph Databases, Brno, 2015.

Gorka Sadowksi, Philip Rathle, Fraud detection: Discovering Connections using Graph Databases, The World’s Leading Graph Database, January 2015.

DB Engines, Knowledge Base of Relational and NoSQL Database Management Systems,, accessed date: March 20th, 2022.