Contact Tracing using Graph Algorithms and exploring graph analytics in a Graph Database (Neo4j)

Abstract

The SARS-COVID-19 epidemic is still spreading rapidly globally. Contact Tracing has played an important role during this emergency in identifying the people most likely to be high spreaders of the virus. Graph Data Science has recently come to the forefront in analysing connected data on a knowledge graph. It included various types of algorithms supporting graph analytics and machine learning workflows. Using GDS we can execute these algorithms we find the optimized result in Neo4j platform This research explores the use of graph data science in the context of contact tracing. Common real-world scenarios are explored using Neo4j, the leading graph database management system. Graph Data Science algorithms including PageRank, betweenness centrality, Louvain community detection, label propagation, shortest path algorithms are explored, and results visualised. Results prove to be insightful.