GitHub serves as both a platform for software development and a social network where users can follow each other based on shared interests and contributions. Analyzing real-time commit data from GitHub can yield valuable insights, which is made possible through tools like Awesome Data Stream and Memgraph. Memgraph, which can be used via Memgraph Cloud or a local instance, enables the dynamic analysis of streaming data using graph algorithms such as PageRank for identifying influential users and community detection for grouping users into communities. The blog explains how these algorithms, which are part of the MAGE graph algorithm library, can be adapted for real-time data by using their dynamic versions to handle constantly changing datasets efficiently. Additionally, the blog provides guidance on setting up triggers to update the graph analysis in real-time and highlights the advantages of using Memgraph as a stream processing pipeline in conjunction with data streams like Kafka, Pulsar, or Redpanda.