Requirements

Distribute graph computation across multiple nodes using frameworks like Spark GraphX or Pregel for parallel algorithm execution.
Support a library of graph algorithms including: pathfinding (Dijkstra, A*), community detection (Louvain), centrality (betweenness, closeness), clustering, and traversals.
Ingest graph updates in real time via a streaming pipeline (e.g. Kafka) so analysis reflects current state.
Support indexed lookups on node/edge properties for fast retrieval at scale using a graph database.
Scale horizontally to accommodate billions of nodes and edges by adding machines to handle growing data.

List the key non-functional requirements (eg low latency, scalability, reliability, etc.)...